Podcasts about lufs

In episode 631, Lisa explores luteinized unruptured follicle syndrome (LUFS) — a lesser-known ovulatory dysfunction in which the dominant follicle fails to rupture and release the egg despite a normal-appearing LH surge and progesterone rise. Because LUFS can mimic a textbook ovulatory cycle on a fertility awareness chart, it represents one of the more challenging silent contributors to unexplained infertility that practitioners may encounter in clinical practice. This episode offers fertility awareness practitioners essential clinical context for supporting clients with apparently ovulatory charts who continue to face unexplained fertility challenges. Follow this link to view the full show notes page! This episode is sponsored by Lisa's new book Real Food for Fertility, co-authored with Lily Nichols! Grab your copy here! Would you prefer to listen to the audiobook version of Real Food for Fertility instead?

This show has been flagged as Clean by the host. 01 Introduction This is a follow up to my 4 part series on simple podcasting. In this episode I will discuss a number of experiments with audio filtering. These experiments were inspired by comments by listeners and by other discussions about audio on HPR. I am not an audio expert, so I am doing this partly in order to learn something, but mainly in order to have a bit of fun. I hope that you find this entertaining as well. In a comment on the first episode a listener mentioned something called Solocast and said that the method bore a resemblance to the method that I was using. Here is his comment -------------------- 02 Comment #3 posted on 2026-04-03 07:49:58 by Reto It reminds me about Solocast Hi Whiskeyjack, I really liked your podcast and the topic. I cannot remember about your last, but the sound quality of this one was good on my mobile speakers :) The concept reminded me about the program from Norrist (another host on HPR), while similar does it have some differences HPR 3496 https://hackerpublicradio.org/eps.php?id=3496 As I am not on the future feed, I look forward to your next episode. Cheers, Reto -------------------- 03 End of comment. I did not recall having heard the episode on Solocast, but this sounded very interesting. Solocast was in HPR episode 3496 and was released by norrist on the 27th of December 2021. I listened to that episode and does indeed use use the same basic concept of recording short segments of audio and combining them later instead of creating one big recording and editing it with an audio editor. 04 The main difference is that the work flow that I described involves a lot of manual steps, while Solocast is a short Python program that automates the entire process of presenting your script, recording the segments, combining the segments, and filtering and normalizing the result. I won't try to describe Solocast in detail, instead I would recommend just listening to HPR episode 3496 to get norrist's explanation directly. -------------------- 05 While I wanted to make sure that I credited norrist with having come up with this concept four years before I did, this won't be the focus of this episode. Instead I will talk about audio filtering and various experiments that I ran on several different methods. 06 While looking at the source code for Solocast I noticed that it used a filtering method that resembled one used by Jivetalk, a podcast production program that caught the attention of one of the HPR community news presenters. This method involves taking a sample of quiet audio where there is no speaking taking place, and then using this as input to a noise reduction filter which is applied to the voice recording. The filter subtracts the quiet sample from the voice audio, which should theoretically remove the ambient noise. 07 I decided to apply this method to a number of different audio test recordings which were recorded under different circumstances using different hardware. In this way I could see if the method worked equally well under all circumstances or if there were some sorts of noise which it was suited to and some sorts that were not. 08 While I was at it, I also picked several other filter methods to see how they worked as well. Potentially, some methods may be better under some conditions while other methods were better suited to others. -------------------- 09 I won't present all of my experiments, as that would be a bit dull to listen to. Instead I will describe each method and then present audio samples which illustrate my conclusions. There are two pieces of audio software involved, both of which were also used in my series on simple podcasting. 10 The first is Sox, spelled s o x , and which is short for Sound Exchange. Sox is a command line program for audio manipulation. Sox is Free Software, released under the GPLv2 or later. The other is FFMPEG, which is also a command line program. FFMPEG is also Free Software, released under the LGPL V 2.1 or later, and GPL v 2 or later. Sox actually uses FFMPEG for certain operations. -------------------- 11 Audio Hardware For recording hardware I used the following. 12 Maxwell Headset The first is a cheap Maxwell headset that has an electrical noise problem. Unfortunately I don't have a model number for this headset. I described this hardware, the noise problems that I had with it, and how I created filters to deal with the noise in my series on simple podcasting. Briefly though, this is a headset that has a build in microphone on a boom which allows the microphone to be positioned close to the mouth. It connects with a USB cable. 13 Borne Earpiece and In-line Microphone This is a set of earplugs that go in your ears and connected by wires and a very small microphone built into a small bulge in the cable. It connects using a 3.5mm jack. The model number seems to be BUD250-BL. 14 XTrike Headset This is a gaming headset similar to the Maxwell headset described above. The model number is GH-510 It uses a USB connection. 15 Yanmai Condenser Microphone This is a microphone that comes with a small tripod stand. The model number is SF-910 It uses a 3.5mm audio jack. -------------------- 16 This is not a review of the hardware. Rather, I was trying to create audio problems so that I could test ways to fix them. Therefore, do not take the above list as a recommendation of what to buy. However, you can see that I am not using any expensive audio hardware. If you want to make an HPR podcast, you do not need professional level hardware. -------------------- 17 Audio Samples The audio samples are as follows 18 Quiet This was recorded in a quiet environment at my desk. This is my normal podcasting environment and represents optimal conditions. The main reason for this method is to see how the various filter methods perform when dealing with the electrical noise from the Maxwell headset. 19 Small fan This is a small USB powered table fan approximately 10 cm in diameter. It was located roughly 40 cm or less to the left of the microphone, although this varies depending on the microphone. 20 Traffic This was along a busy street with traffic noise in the background. -------------------- 21 Filter Methods Sox noisered Filter with Audio Profile This method uses the Sox noisered filter. Here is a brief quote from the Sox documentation on this filter. Quote Reduce noise in the audio signal by profiling and filtering. This effect is moderately effective at removing consistent background noise such as hiss or hum. To use it, first run SoX with the noiseprof effect on a section of audio that ideally would contain silence but in fact contains noise - such sections are typically found at the beginning or the end of a recording. End of quote For these tests I recorded a separate noise profile to go with each test. -------------------- 22 Basic Manual Filter This is a basic high and low pass filter pair based on the work I had done in my previous series on simple podcasting. However, based on the tests that I have done for this episode, I decided to get a bit more aggressive in terms of filtering. I use a high pass filter of 120 Hz, and low pass filter of 8 kHz. The each filter is then applied twice to increase its effect. I also added band reject filters to deal specifically with 50 and 60 Hz line noise. -------------------- 23 Complex Manual Filter This uses the manually constructed filter described in my series on simple podcasting. This uses the basic manual filter plus a series of custom bandreject filters to fix specific noise problems with the Maxwell headset. -------------------- 24 FFMPEG afftdn Filter The documentation describes this as "Denoise audio samples with FFT." -------------------- 25 FFMPEG arnndn Filter The documentation describes this as "Reduce noise from speech using Recurrent Neural Networks." -------------------- 26 FFMPEG agate Filter I will pronounce this as "agate" for convenience. The documentation describes this as "A gate is mainly used to reduce lower parts of a signal. This kind of signal processing reduces disturbing noise between useful signals." -------------------- 27 Method The experimental method used was to take each noise sample and apply the different filter methods to it. Where there are parameters which can be adjusted, a script was used to generate a series of different sample files with different parameter values. Not all possible parameters were experimented with, as the goal is to see which method produces what sorts of results under different circumstances, not to get the best possible result for the samples that I happen to have. The method in each case was as follows 28 Step 1 Convert the audio file to FLAC if it is not already in that format. 29 Step 2 Apply a basic high and low pass filter described previously to each sample. The reason for this basic filtering is that it eliminates at least some undesired noise in a fairly fool proof manner, leaving less for the more advanced filter to deal with. This should allow for a better test of the filter under realistic conditions. 30 Step 3 Apply the noise reduction filter being tested. 31 Step 4 Normalize the filtered sample to 17 LUFS according to the EBU R128 standard. The EBU standard is described in my series on simple podcasting. Normalizing adjusts the audio signal to a desired loudness level. This allows for more more consistent sound levels and allows us to hear the results under realistic conditions. I normalize the audio individually for each sample as different recording hardware requires different amounts of loudness adjustment. This is different from the typical podcast process where normalizing takes place as the very last step in the process, but it was necessary in this case. 32 Step 5 Concatenate selected sample audio files to one another to allow for better review and comparing. -------------------- 33 Results The results are grouped according to the type of noise which is being mitigated. This allows for easier comparison of the effectiveness of each technique under different circumstances. I have only picked a few examples of interest out of the numerous experiments that I conducted. -------------------- 34 Quiet Recording Environment with Maxwell Headset This compares how well the various filtering methods work on the noise induced by the electronics in the Maxwell headset. This electronic noise consisted of a noise spike every 1 kHz. This should be representative of electronic noise caused by problems in recording hardware. 35 Manual Filter The manual filter applied a narrow band reject filter every 1 kHz from 1 kHz to 12 kHz. This completely removed the otherwise audible whine caused by the noise. 36 FFMPEG afftdn This method allows for setting a noise floor and then specifying how much the noise floor should be reduced by. The method is very sensitive to getting the noise floor correct for that recording. Set the floor too low and nothing happens. Set it too high, and some distortion results. However it seemed to be moderately effective, but it would seem to require checking it and possibly adjusting it each time it is used. 37 FFMPEG agate This method allows setting a noise floor and then suppressing all sound which falls below that level. This method is very sensitive to getting the noise floor correct for that recording. If set too low (or quiet), it is ineffective. If set too high (or loud), it distorts words which come after a pause, which would typically be between sentences. 38 When set correctly, it completely removes noise in the silences between sentences. However, the noise is still audible during speech. This is because the noise in this case is a higher frequency than normal speech, and so stands out more. It may not be a significant problem for noise which is closer to the main vocal frequency band. Overall, this method is not suitable for this particular problem. 39 FFMPEG arnndn This method used the standard model. A variety of different noise reduction models are available. I only tested it with one, std.rnnn It does not seem to introduce much distortion in the voice signal even with a high amount of mix parameter. 40 However, it is only slightly effective at removing the whine from the signal, even with a high amount of mix parameter. Overall, this method does not appear to be useful for this sort of noise problem. 41 Sox noisered Filter This was effective in removing noise between words, but noise can be heard while words are being spoken. It was better than agate however. 42 Overall Conclusion for the Maxwell Headset Noise When dealing with narrow noise bands that occur at known frequencies, the manual filter is leagues ahead of any of the other tested alternatives. 43 Sample Audio Here is a sample audio recording showing the best overall results The sample is repeated, first with only basic low and high pass filtering, and then with the manually constructed filtering. In the first sample you should hear a high pitched background whine. In the second sample, the high pitched whine is completely removed. 44 (Audio sample inserted here.) -------------------- 45 Traffic Noise This was recorded using the Borne in-line microphone connected to a mobile phone while walking along beside a busy street. This was in dry cool spring weather, and the road was paved with asphalt. This should be reasonably representative of podcasting while walking outdoors in a noisy environment. 46 Basic Manual Filter This used the basic manual filter with high and low pass filters. This did nothing very useful in this case as the signal was already filtered within those limits by the recording hardware anyway. The low sample rate of 8 kHz in the phone limited the upper frequency to 4 kHz. Recall that the sample rate has to be twice the highest frequency that you want to detect. Overall, this is not suitable for this sort of problem. 47 FFMPEG afftdn With a high noise floor, background noise is reduced, but not eliminated. There was not much distortion in the voice. This is only slightly useful for this sort of problem. 48 FFMPEG agate With a high threshhold, background noise is reduced, but not eliminated. There was some distortion in the voice. The background noise could also be heard when speaking, but because the frequency of the background signal was similar to the louder voice signal, it was not as noticeable as it would have been if the two were very different. This is moderately useful for this sort of problem. It may be more useful in situations where the background noise was not quite as loud. 49 FFMPEG arnndn With high amounts of noise reduction, much of the background noise is suppressed, but there is not a lot of distortion in the voice. The background traffic noise is still present, but is significantly less. This offers only a moderate improvement. 50 Sox noisered Filter With small amounts of noise reduction voice is clear but traffic noise is present as a very significant continuous warbling sound in the background. This is no improvement on the original and in fact could be seen as making it worse. With moderate amounts of noise reduction, traffic noise is mostly gone, but there are still various squeaks present. Voice is noticeably distorted. With large amounts of noise reduction, traffic noise is gone but voice is highly distorted. This is moderately useful for this sort of problem, but requires careful adjustment. 51 FFMPEG arnndn Followed by FFMPEG agate This combined two different filters. First, it used arnndn to suppress the background noise to a lower level without much voice distortion. Then it applied the agate filter to suppress the noise levels between words still further. This used the same amount of mix and threshold as was found to be most effective when each of these filters was used on its own. The background noise is almost completely gone while distortion of the voice signal is low. 52 Overall Conclusion for Traffic Noise The arnndn combined with agate filters was the most successful at suppressing background noise while limiting the amount of voice signal distortion. 53 Sample Audio Here is an audio sample for what I felt to be the best overall results, the arnndn filter combined with the agate filter. First is the original audio with basic filtering. This is followed with the same audio after being passed through the arnndn and agate filters. 54 (Insert arnndn plus agate audio sample here) 55 Another Sample Here is a second audio sample showing the Sox noisered profile based filter. I have included this to show how a profile based filter can make things worse if you are not careful how you use it. This repeats the test audio 4 times. The first is with basic filtering only. The second uses low amounts of noise reduction. The third uses moderate amounts of noise reduction. The fourth uses high amounts of noise reduction. 56 (Insert noisered audio sample here) -------------------- 57 Small Fan Noise with Yanmai Microphone This was recorded using the Yanmai condenser microphone. A small fan was set up behind and to the left of the microphone. This is intended to represent situations where someone may have a fan or air conditioner running in the background due to hot weather, or has a loud computer fan. 58 A condenser microphone was used for this test as they are more prone to picking up unwanted noise. However, for practical recording purposes, this sort of microphone is unsuitable for this type of environment. 59 Basic Manual Filter This used the basic manual filter with high and low pass filters. This did nothing useful as the fan noise was in the same frequency range as the voice signal. This may be of more help in cases where the noise is below the 120 Hz cut off used in the low pass filter. 60 FFMPEG afftdn With high amounts of noise reduction, much of the background noise is suppressed, but there is some distortion in the voice. The background fan noise is still present, but is significantly less. Overall this is moderately effective. 61 FFMPEG agate This was effective in removing noise between words, but noise can be heard while words are being spoken. However, this was a small voice sample and it is possible that more problems could occur. With less fan noise than was in this sample this technique may work much better. 62 FFMPEG arnndn With high amounts of noise reduction, much of the background noise is suppressed, but there is not a lot of distortion in the voice. The background fan noise is still present, but is significantly less. Overall this was fairly effective. 63 Sox noisered Filter With small amounts of noise reduction voice is clear but fan noise is present as a slight warbling sound in the background. With moderate amounts of noise reduction, fan noise is gone, but voice is somewhat distorted. With large amounts of noise reduction, fan noise is gone but voice is very distorted. 64 In general this method is fairly successful at dealing with this sort of problem. However, there is a trade off between background noise and voice quality. Getting that trade off correct takes experiment and judgment for each specific situation. 65 FFMPEG arnndn Followed by FFMPEG agate This combined two different filters. First, it used arnndn to suppress the background noise to a lower level without much voice distortion. Then it applied the agate filter to suppress the noise levels between words still further. This got rid of virtually all of the background noise between words. If you listen carefully however, there is a slight buzzing sound in the voice signal. 66 Overall Conclusion for Fan Noise with Yanmai Microphone. Of the methods tested, the arnndn followed by agate filter seemed to offer the most improvement for the least effort and least voice distortion. The arnndn filter on its own seemed the next most preferable to me despite leaving some fan noise in the background. 67 Audio Sample Here is an audio sample for what I felt to be the best overall results, the arnndn filter combined with the agate filter. First is the original audio with basic filtering. This is followed with the same audio after being passed through the arnndn and agate filters. 68 (Insert audio sample here) -------------------- 69 Small Fan Noise Recorded with Headset The following is an observation rather than a filtering technique. When a recording was made using the Maxwell headset and listened to on the headset later or with speakers, the fan was virtually inaudible. When the same recording was listened to with the XTrike headset, it was barely audible with careful listening and only identifiable as a fan because I knew it was there. 70 In situations where there is ambient noise, the best noise reduction technique is probably to move the microphone as close to your mouth as possible, although not directly in front of it, and reduce the gain if there is a gain adjustment in the microphone. This will work far better than trying to remove the noise later. If you are recording an HPR episode at a desk, then an inexpensive headset with boom mike may do the job just fine with minimal effort and expense. -------------------- 71 Conclusions I have tested three noise scenarios - Electronic noise in the audio hardware at specific frequencies. Recording outdoors with an inline microphone in a noisy traffic environment. A noisy fan creating background noise in an office. My conclusions on these are as follows. 72 Electronic Noise in the Audio Hardware at Specific Frequencies If you can use Audacity or some other means to find the frequencies which are causing the noise, the best solution, assuming you don't just replace the hardware, is to manually construct filters to remove those specific frequencies. This is the safest solution in terms of only doing what you tell it to and not producing unexpected surprises some time down the road when something changed in the environment. 73 If you are looking for a fairly automatic filtering method, the Sox noisered profile based filter seems to work fairly well. There is an equivalent filter in ffmpeg, but I did not include that in my experiments as it is harder to use in a script because it does not use a separate noise profile file. 74 Recording Outdoors with an Inline Microphone in a Noisy Traffic Environment. In this situation, the FFMPEG arnndn combined with agate filters seem to be the most successful. The Sox noisered filter may work, but at the cost of more distortion in the voice than is seen in the other methods. 75 An inherent problem with any profile based noise reduction method is that if the background noise is not constant, which it seldom is in that sort of environment, the profile may not represent the background noise which is present later on in the recording. This risks adding more distortion in the voice as the profile and later environments diverge. 76 However, for this application a different microphone that provided a better recording would appear to be advisable. A solution which brought the microphone much closer to the mouth and so resulted in a better ratio of voice signal compared to background noise would appear to be necessary, after which the question of what sort of noise reduction to use would need to be re-evaluated. 77 A Noisy Fan Creating Background Noise in an Office. The Sox noisered filter and the FFMPEG arnndn, afftdn, and agate methods all work to some degree. However, they all need correct selection of parameters to achieve the proper results. When I compared all four methods side by side, I found the arnndn combined with the agate filter to be preferable in terms of the trade off between background noise reduction and distortion of the voice signal. The arnndn filter on its own seemed the next most preferable to me despite leaving some fan noise in the background. 78 However, that is a subjective judgment of a specific noise sample when recorded using a specific microphone. Keep in mind though that many listeners will not be listening in an idea environment. They may be doing things where background noise is present rather than in a very quiet room and so may find a small amount of background noise in the recording to be less of a problem than distortion in the voice signal which may make some words harder to understand. 79 When I conducted the same experiment recorded with the XTrike headset I found that arnndn seemed to offer no noticeable improvement. This may be because the amount of audible fan noise was far less with the XTrike headset to begin with. In other words, there is no single best solution here, and you may have to be prepared to try different options to see which one works in your situation. The important thing is to avoid making things worse by applying filtering that is not appropriate for that situation. The best method may be to use a recording method that doesn't pick up the fan noise to begin with. This can include just using a gaming headset with boom mic. 80 I have one final observation on this point regarding headsets. The Maxwell headset has a foam cover over the microphone while the XTrike headset does not. There was some slight audible wind buffeting noise picked up by the XTrike headset that was not observed with the Maxwell. This seemed to cause particular problems with the Sox noisered profile based filter, as this noise was irregular and after filtering would show up as a warbling sound. If you use a headset and plan to use it in conjunction with a fan, it may be advisable to apply some sort of wind cover over it. 81 Combining Complex Filters In several cases I found that combining several complex filters offered better results than using any single one on its own. The basic strategy though is to first use a method which is good at reducing undesirable noise without introducing excessive voice distortion. Then apply a different filter which is good at reducing small levels of background noise to an even lower level while affecting the voice signal as little as possible. This uses the relative strengths of different filter types to compensate for the weaknesses of the other. 82 Different combinations of filters were most effective for different types of problems. I did not try all possible combinations however. Perhaps a further exploration of this would be worth doing in a later podcast. -------------------- 83 Case Study - Noise in Another HPR Episode Audio In the comments to my second episode on Simple Podcasting (which is HPR4618) where I discussed basic filtering, a couple of listeners brought up an interesting point. Antoine mentioned "declicking" in a post. -------------------- Vance replied 84 Antoine, thanks for mentioning the click removal capability in Audacity! While I already knew about its noise removal filter, I wasn't aware it also had click removal. It might have helped me for HPR4637, where some sort of electromagnetic signal was picked up by my microphone/recorder, a Zoom H2 (the tapping sound was *not* present in the room where I recorded). While click removal does seem to distort speech when applied to it (though to my ears, it doesn't sound as weird as when noise removal is done with speech), I could have applied the filter only to the pauses, where the "tapping" is most noticeable. I will consider doing this in the event that I'm not able to eliminate the source of interference in the future, which would be the best way to go. -------------------- 85 End of quote. I found this interesting as it sounded like another audio problem that could be experimented with. I found a sample of the episode which had the clicks and cut a copy of that segment out to experiment with. These sounds are a series of clicks, or "ticks" would be another way to describe them, in the quiet part of the audio between sentences or phrases. 86 Next I used Audacity to study the sound spectrum. I found a massive 60 Hz noise spike. However, my speakers won't reproduce sound that low, and filtering this out didn't reduce the clicks. The clicks turned out to be bursts of noise across the 100 to 800 Hz band, which is right where the main vocal band also is. This makes it difficult to filter based on frequency. The most promising approach would seem to be to filter based on sound level. 87 I tried all of the individual audio filter techniques mentioned in the other experiments above. None produced satisfactory results except for agate, which makes quiet audio quieter. This completely suppressed the clicks. However, when applied to the entire episode it also distorted the start of a few sentences which began with single short syllables. 88 The agate filter has a number of parameters which could be adjusted to try to deal with these cases, although I did not spend the time to do so. Another solution to this distortion problem is to simply not apply the filter to those parts of the audio which are affected. If you record the audio as a series of small individual files, it would be easy enough to filter before concatenating the files together while skipping those files which contain audio which is not suited to this method. Here are the results of the experiments. 89 FFMPEG afftdn This reduces the size of of the ticks, but they are still present. However, they may be reduced to a level which is considered acceptable. 90 FFMPEG agate This was very effective in removing ticks with the right parameters. However, it can introduce some voice distortion in the form of cutting out the start of a few sentences which began with single short syllables. This can be corrected with a very short "attack" parameter to turn off the filter when it detects sound above a set threshhold. 91 FFMPEG arnndn This was relatively ineffective. 92 Sox noisered This was effective in removing the sounds between phrases. However, it introduces some distortion in the voice signal. 93 I also tried combining filters. FFMPEG afftdn Followed by agate This combined two different filters. First, it used afftdn to suppress the background noise to a lower level without much voice distortion. Then it applied the agate filter to suppress the noise levels between words still further. This got rid of virtually all of the background noise between words. 94 Here is a short audio sample from HPR4637. First is the unfiltered audio. Second is the filtered audio using the combined afftdn plus agate filters. Since the "clicks" are very quiet, you may not hear them unless you are in quiet environment. Quite a few listeners would probably not be aware of the perceived audio problem in this episode if it had not been discussed here. None the less, it makes for an interesting experiment. Here it is: 95 (Insert sample audio here) 96 Overall Conclusion for Noise "Ticks" The afftdn combined with agate filters seemed to offer the best overall results when used with the right parameters. However, the author, Vance, speaks very clearly and evenly, and so his voice is ideally suited for use with this filter. Another author's voice may not be as suited to this filter. 97 The Sox noisered profile based filter offers various degrees of trade off between suppressing noise and distorting the voice signal. As to whether this is an acceptable trade off depends on the particular voice in question and how easily understood it is under normal circumstances with out additional distortion. The afftdn filter may be a fairly safe filter to use on its own while producing acceptable if not perfect output. -------------------- 98 Overall Conclusions I have presented only a few of the experiments that I conducted. My overall conclusion after all of this is that there is no universal audio filtering method that works best in all circumstances. There are instead a number of tools in the toolbox, and picking the right one for the job takes a bit of trial and error. 99 However, if you have a repeatable recording environment, then once you have decided what tool you need you should create a script for it so you can have a repeatable processing setup. These conclusions apply to voice podcasting. Music has a different set of criteria and techniques that work well with basic voice podcasting may produce poor results when applied to music which has a broader range of frequency and just as importantly, a broad range of loudness. 100 If you are used to using filters and effects in Audacity, many of the settings on those correspond to arguments in the command line version of ffmpeg. It is worth learning how to use ffmpeg directly to automate your recording process. 101 The experiments that I conducted were greatly assisted by writing scripts which created multiple versions of audio files with different settings, thereby allowing me to try many different alternatives relatively easily. It also allowed me to concatenate different audio samples into a single audio file and so listen to different versions in quick succession, making subjective listening judgments more reliable. 102 It is important to keep in mind in all this that I am playing with audio filtering mainly to have fun. It is not necessary to do any of this if you think your podcast episode sounds just fine without it. So, don't let any of what I have talked about in all this discourage you from simply recording a podcast and sending it in as is. I will include copies of the filters I have described here in the show notes. -------------------- 103 Related Matters Hardware Characterization Using Audio Signals I found it useful to characterize the hardware that I had in order to understand its limitations better before starting the experiments. This involved playing a signal out through a set of speakers and then recording it through a microphone. 104 I used two types of signal for this. One is type of signal is known as a "chirp" signal. This is a sine wave that steadily increases in frequency as it sweeps across the audio spectrum. The standard audio range is 20 Hz to 20 kHz, but for my purposes I limited the upper frequency to 15 kHz to save time as anything beyond that is not very useful for voice podcasts. 105 By recording the chirp signal with a microphone and analyzing it with a Fourier transform, I could quickly see what each device was capable of. See my previous series on simple podcasting for an explanation of what a Fourier transform is and what software to use to see the results of it. Here is a chirp signal. 106 (Insert Audio Sample Here) 107 In addition to a chirp signal, I also used a series of simple tones of specific frequencies. By using these tones of known frequency I could gain an understanding of the limitations of my speakers and headphones, and just as importantly, my own ears. By understanding these limitations I was able to narrow the range of frequencies that I need to deal with quite considerably and set the high and low pass filters accordingly. These tones are a series of flac files generated with ffmpeg. 108 Here is a a sample audio tone at a 2 kHz frequency. 109 (Insert Audio Sample Here) 110 Copies of the script to create the chirp signal and the tones are in the show notes. -------------------- 111 A "Not a Review" of some of the Hardware that I Used I said that I would not do a review of the hardware that I used. However, some of it deserves mention for either how good or bad it was. I will record each section using the hardware being described. 112 Maxwell Headset This is my original recording hardware. This is a headset with boom mic and USB connection. There is no model number on it, so I don't know the model. This probably cost somewhere between 10 and 25 dollars. The earpieces sit on the ears and do not fully enclose them. This makes it light weight and comfortable to wear for extended periods of time. It has a problem however with electronic noise consisting of a noise spike every 1 kHz. I was able to fix this with a series of filters using FFMPEG. Fixing this problem is what got me started in understanding audio. I will probably continue to use this headset to make podcasts. 113 XTrike Headset, Model GH-510 This is also a headset with boom mic and USB connection. I purchased this headset for the purposes of experimentation for this podcast episode. It cost $12.88. I found it to be surprisingly good for the price. It has fully enclosed ear pieces however, which may make it uncomfortable to wear in hot weather. I may try doing some of my future podcasting using this headset. 114 Borne Earpiece and In-line Microphone This is a set of earplugs that go in your ears and connected by wires and a very small microphone built into a small bulge in the cable. It connects using a 3.5mm jack. The model number seems to be BUD250-BL. It cost approximately $3.00. I bought several sets of these and use them for listening to podcasts from an MP3 player. The ear pieces are pretty good for listening with. The microphone works reasonably well when used in a quiet location. It is less good when in a noisy environment. It is very important however to secure the microphone to your lapel or other location reasonably near your mouth and to point the microphone (that is the small hole) outwards and not simply let it dangle freely. If you let it just hang, you will get poor quality and inconsistent audio. 115 Yanmai Condenser Microphone, Model SF-910 I purchased this microphone for the purposes of experimentation for this podcast episode. It cost $3.88. As it is a condenser microphone, it is prone to picking up background noise more and as such is probably not a good choice for podcasting by single person sitting at a desk. However, it is none the less a surprisingly good microphone for surprisingly little money. 116 iCan USB Microphone, Model M-306 I purchased this microphone for the purposes of experimentation for this podcast episode. This has a USB connection. This was also relatively inexpensive at $7.99, or roughly twice the price of the Yanmai microphone. Unlike the Yanmai however, it is absolutely wretched. There was such a high degree of distortion when recording through it that I found I could not use it in the fan experiments which I had bought it for. I ended up buying the Yanmai microphone for that instead. -------------------- 117 Easy Effects Software The techniques described so far all involve recording audio files and then processing them later to produce the desired result. This is probably the simplest and most straightforward way of doing things if you are making a typical podcast. However, there may be instances where you want to apply filtering or other effects on the "live" signal immediately and not after the fact. 118 There is audio software which can hook into your computer's audio system and do this with a live signal. For Linux, there is a package called "Easy Effects". This is Free Software and comes under a GPL V3 or later license. I installed it from the Debian repository under Ubuntu 24.04. 119 You can create various filters and even chain them together to combine them. I played with it a bit but do not know enough about it to discuss it seriously at this time. However, I thought it would be worth mentioning for the sake of those who may wish to try it out themselves. -------------------- 120 Episode Conclusion After having had some fun with audio and listening to other HPR members talk about audio, I thought I would have some more fun by playing with noise reduction filters. I have no intention of becoming an audio professional, but by doing some experiments I learned a few things and had some fun doing it. I hope that the rest of you found this interest as well. I will see you all again later in another episode of Hacker Public Radio. -------------------- Scripts Basic Filter This shows basic high and low pass filters ( 120 Hz and 8 kHz respectively) and band reject filters for 50 and 60 Hz. # The high and low pass filters. hlpfil="highpass=f=120, highpass=f=120, lowpass=f=8000, lowpass=f=8000" # Band reject filters filter for 60Hz and another for 50Hz. linefil="bandreject=f=60:width_type=h:w=20, bandreject=f=50:width_type=h:w=20" # Filter using ffmpeg. ffmpeg -i inputfile.flac -af "$hlpfil, $linefil" outputname.flac # ====================================================================== afftdn Filter # noisefloor should be between 20 and 80. noisefloor=$1 # Run the noise reduction. ffmpeg -i testrec-filtered.flac -af "afftdn=nr=10:nf=-""$noisefloor" tmptestrec.flac # ====================================================================== agate Filter # threshold shoud be between 10 and 80. threshold=$1 # Run the noise reduction. ffmpeg -i testrec-filtered.flac -af "agate=threshold=-"$threshold"dB:range=-60dB" tmptestrec.flac # ====================================================================== arnndn Filter # mix should be between 0 and 1. mix=$1 # Run the noise reduction. ffmpeg -i testrec-filtered.flac -af 'arnndn=model=std.rnnn:mix='"$mix" tmptestrec.flac # ====================================================================== sox noisered Filter # Generate the noise profile from a sample of background noise. sox silencefiltered.flac -n noiseprof noise.prof # nramount shoudl be between 0 and 1 sox testrec-filtered.flac noiseout-testrec.flac noisered noise.prof "$nramount" # ====================================================================== Manual Filter for Maxwell Headset Noise # Create a series of band reject filters, from 1 kHz to 11 kHz. ftemplate="bandreject=f=%s000:width_type=h:w=100" kilospikefil=$( seq 1 11 | xargs printf "$ftemplate," ) # Using ffmpeg ffmpeg -i testrec-filtered.flac -af "$kilospikefil" tmptestrec.flac # ====================================================================== Create a "chirp" signal # Start frequency. f0=20 # End frequency. f1=15000 # Duration of signal. duration=10 ffmpeg -f lavfi -i "aevalsrc=sin(2 * PI * (0.5 * ($f1 - $f0)/$duration * t^2 + ($f0 * t))):s=44100:d=$duration" -c:a flac -af "aformat=sample_fmts=s16" chirp.flac # ====================================================================== Generate Audio Tones toneout () { printf -v freqval "%05d" $1 ffmpeg -f lavfi -i "sine=frequency=$freqval:duration=3" tmptone.flac # Normalize ffmpeg -i tmptone.flac -af loudnorm=I=-17:TP=-2.0:LRA=4.0 -ar 44.1k -sample_fmt s16 tone$freqval.flac rm tmptone.flac } # List of frequencies in hertz. freqlist="50 60 100 120 130 140 150 160 170 200 500 1000 2000 3000 4000 5000 6000 7000 8000 9000" for freq in $( echo $freqlist ); do toneout $freq done # ====================================================================== Provide feedback on this episode.

1)Ruma, Adrian Forsén - Sun Comes Down (Original Mix) 2)Abstract Vision, Hydro Poison - Of Time & the River (Extended Mix) 3)Daniel Portman, Drilla - Cafe Del Mar (Extended Mix) 4)EDX - Equinox (Extended Mix) 5)Danito, Vemativ - Make You Mine (Extended Mix) 6)Tritonal, Harlee - Walk Away (Bound To Divide Extended Remix) 7)Drilla - Naked Sunday (Extended Mix) 8)SAFARIS - Silver (Extended Mix) 9)LUFS, Milan Steenwinkel - Silence Burning (Extended Mix) 10)SAFARIS - Don't Look at Me (Extended Mix) 11)ALAC - If You Want It (Extended Mix) 12)Idy Ramy - Siberia (Extended Mix)

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

Évadez-vous dans un univers onirique où l'ambition rencontre la sérénité. DREAM LIKE A BOSS est bien plus qu'une simple compilation de beats ; c'est une expérience sonore conçue par The Next Top Beat pour vous accompagner dans vos sessions de concentration les plus intenses ou vos moments de repos les plus profonds.

This show has been flagged as Clean by the host. Basic-Filtering 01 Introduction This is the second episode in a four part series on a simple way to create your own HPR podcast episode. 02 This episode will cover the following topics: Basic filtering.. De-essing to improve voice quality. And normalizing to adjust audio levels for easier reviewing. 03 Filtering is removing unwanted noise from an audio signal. There are several ways of doing this. It is possible to do this with Audacity, but I don't know how so I won't try to describe that method. It is possible however to filter using command line tools such as FFMPEG and Sox. When assembled into shell scripts, these tools can become part of an automated process that you can use over and over again for each HPR episode that you record. 04 In a later episode I will discuss how to analyze audio signals to find the sources of noise that can be reduced or eliminated with filters. In this episode however I will discuss basic filtering that you can apply routinely without doing any analysis beforehand. 05 Sources of Noise A question that you may have is "why is there noise in the recording?" There are many sources of undesirable noise. 06 A very common one that you may not be aware of is electrical noise that works its way into the electronic recording circuits and is imperceptible to you until you play back the recorded audio. The most common noise signal is what is commonly called "line noise" and is a low frequency hum at 50 or 60 Hz from the electric power lines and reflects the 50 or 60 Hz frequency of the AC power lines feeding your recording hardware. 07 You may be familiar with this low frequency hum from when it emanates from large electrical hardware such as transformers as it makes the laminations vibrate. However, it can also work its way indirectly into electronic equipment as well. Good quality audio hardware may filter all or most of this out, but it is present in a lot of consumer grade hardware. 08 Other sources of electrical noise may reflect specific problems in your recording hardware. I will discuss one such problem with my microphone that I had to address. Still other sources of noise may reflect actual physical audio noise around you, such as fans. Placing the microphone close to your face will help in dealing with a lot of these problems, but you may find filtering to be of some help here as well. 09 Audio Frequency Range Let's start with some basics. A good quality stereo of the type you may have at home is typically rated to perform between 20 Hz and 20 kHz. This is the widest possible range that we need to consider. In reality, this is a far wider range than is needed for a voice oriented podcast. It is also well beyond the range of the hardware that many of your listeners will be using to listen to the podcast. 10 For example, the speakers that I have connected to my PC and a number of headphones and earphones that I have tested drop off drastically below 80 Hz or above 8 kHz, or even above 6 kHz in many cases. This is not audiophile quality hardware, but it is representative of the sort of hardware that a lot of your listeners will be using when listening to podcasts. And to be honest here, a lot of people will have difficulty hearing anything above 8 kHz even with the best quality audio hardware due to hearing loss from environmental noise exposure or age. 11 You can get a good idea of what different frequencies sound like by generating sine waves using either FFMPEG or Sox. Here's an example of generating a 1 kHz sine wave using FFMPEG. A copy of this will be in the show notes. ffmpeg -f lavfi -i "sine=frequency=1000:sample_rate=44100:duration=3" 01000hz.flac This creates a sine wave at 1 kHz and at a sample rate of 44.1 kHz for a duration of 3 seconds and saves it to a flac file named 01000hz.flac 12 Here's the same using Sox. sox -n -r 44100 -b 16 01000hz.flac synth 3 sine 1000 The -b 16 specifies using 16 bit audio to encode it, and the "sine 1000" element specifies the frequency in hertz. 13 You can test this out at different frequencies to get a feel for how your hardware responds. What the effective limits on typical hardware audio range means is that we can quite safely filter out a large part of what is considered to be the "audio range" without any noticeable loss of quality. For the purposes of our discussion here then I will limit the frequency range to between 80 Hz and 12 kHz, and that is being generous. You can probably narrow that, particularly at the top end, without any problems. 14 At the low end, the typical rule of thumb recommended by most people seems to be that for the average male voice you can set the lower threshold at 80 Hz, and for the average female you can set it at 160 Hz. Note that you don't *have* to set the threshold higher for a female. Rather, it is just that you typically *can* set it higher if you wish. Note also that these are averages, and may not reflect an actual individual. 15 Simple Filters We will now create some simple filters using the same command line software mentioned in a previous episode in this series. These are FFMPEG and Sox. 16 First let's define some terminology. A high pass filter passes through frequencies which fall above a certain threshold and blocks frequencies which are below that frequency. A low pass filter passes through frequencies which fall below a certain threshold and blocks frequencies which are above that frequency. 17 In reality there isn't an abrupt cut-off in the filters. Instead there is a gradual roll off or sloping off of amplitude below or above the specified filter frequency. This is for two reasons. One is that if there was an abrupt cut off then it would risk introducing audible distortion in the signal for frequencies on the margin. 18 The other reason is that this is how hardware filters traditionally inherently worked when they were made out of electronic components such as resistors, capacitors, and inductors. The sharpness of this cut off can be adjusted, but we won't be fiddling with it in that sort of detail. You will sometimes see filters specified in terms of "poles". This has to do with describing how filters were constructed using electronic components. Don't worry about it, it doesn't really matter. 19 Here is a typical high pass filter using ffmpeg which filters out frequencies below 80 hertz. # High pass filter. ffmpeg -i inputfile.flac -af "highpass=f=80" outputfile.flac Here is a typical low pass filter using ffmpeg which filters out frequencies above 12 kHz. # Low pass filter. ffmpeg -i inputfile.flac -af "lowpass=f=12000" outputfile.flac 20 Here is a filter which combines the two. # Combined filters. ffmpeg -i inputfile.flac -af "highpass=f=80, lowpass=f=12000" outputfile.flac And here is the same thing using Sox. sox inputfile.flac outputfile.flac highpass 80 lowpass 12000 21 Filtering Out Specific Frequencies Recall that I mentioned that a common source of noise is the 50 or 60 Hz AC power line frequency working its way through the electronics of your recording device. Because filters operate gradually and the 80 Hz lower filter threshold is close to 60 Hz, the high pass filter may not deal with this adequately. 22 Now it happens that your listeners may not be able to hear this 50 or 60 Hz noise anyway because their audio hardware won't reproduce it. That by the way includes you not being able to hear it either when you review your recording before uploading it. However, there may be some HPR listeners who are sitting back sipping a glass of wine and listening to your episode on their stereo and who can hear it. That suggests that we ought to do something about it just in case. 23 I will get into how to analyze audio signals in a later episode, but for now just accept that I looked at the frequency spectrum of a sample recording using my hardware and found a large 60 Hz noise spike which I wanted to address. 24 Experimenting with additional high pass frequencies up to 120 Hz did not improve things much with respect to the 60 Hz problem. There are other parameters which could be tweaked, but at this point it would seem most promising to attack the 60 Hz spike problem directly using a different filter method. To deal with the this 60 Hz spike we can use a "band reject" filter, which removes a specific band of frequencies. We will use this in combination with the filtering that we have already done above. 25 After a small amount of experimenting I came up with the following. I also added in a 50 Hz filter while I was at it, for the benefit of those living in areas with 50 Hz electrical supply. These filters will be included in the show notes, so don't worry if you can't quite understand all the details from a verbal description. 26 Here's the FFMPEG version. # Using ffmpeg ffmpeg -i input.flac -af "highpass=f=80, lowpass=f=12000, bandreject=f=60:width_type=h:w=20, bandreject=f=50:width_type=h:w=20" output.flac 27 This as the following elements A high pass filter at 80 Hz, A low pass filter at 12 kHz, A band reject filter centred at 60 Hz and with a width of 20 hertz. A similar band reject filter centred at 50 Hz. 28 Here's the Sox version. # Sox version. sox input.flac output.flac highpass 80 lowpass 12000 bandreject 60 20 bandreject 50 20 Note that with sox, don't quote the filter definition strings or else it will result in an error as sox doesn't see enough parameters. This is not a problem with ffmpeg. 29 The band reject filter knocks the stuffing out of the 60 Hz line noise, and the 50 Hz parameter should do the same for that frequency as well. This basic filter should be able to be applied to any podcast audio recording without causing any problems. You can probably reduce the low pass frequency from 12 kHz down to 8 kHz without any problems, but I would suggest that you test it with your voice before making that decision. 30 I will come back to filtering out specific frequencies again later when I discuss how I solved a specific problem with the hardware that I am using. However, we have to discuss how to analyze audio signals before we can do that sort of technical troubleshooting, and I will cover that in a later episode. -------------------- 31 De-Essing An additional type of filtering is "de-essing". When recording audio, the microphone or environment may result in "s", "sh", "ch" and possibly other sounds to be exaggerated. These are all higher frequency elements of voice recordings. "De-essing" attempts to soften these sounds by selectively reducing the volume on the frequency band which contains these sounds. 32 Software Filters De-essing is accomplished via software filters. FFMPEG and Sox both have de-essing filters. For FFMPEG, the de-essing filter is built in. For Sox however, we must install an optional plug-in. I will cover this is more detail when I discuss using Sox for de-essing. 33 Do You Need De-Essing? The first thing to make clear however, is that you may not need to worry about this. If you think the audio sounds just fine the way it is, you don't need to do any de-essing to it. De-essing is a very subtle change, and you would probably need to do some careful before and after comparisons of audio samples to tell the difference. I didn't know that a thing called de-essing even existed before I started doing the research to make this podcast episode. However, at this point we are doing things more for fun than out of necessity, so I'll describe it anyway. 34 De-Essing with FFMPEG De-essing with FFMPEG is relatively simple. The filter is built in, and there are just three values to adjust. On the other hand, it is not really obvious what these values mean in practical terms. 35 I will however warn you to not rely on the AI search results from Google to understand this feature. The AI, in my experience, just makes stuff up about it and tells you to use options that don't exist and values that are not valid. I found that the only useful information came from FFMPEG's own web site, and from examples written by actual humans. 36 I then experimented with different values to see what effects they had. Since the results are rather subtle, fine tuning isn't really that necessary and I found that I could arrive at some reasonable values fairly quickly. I will provide the parameters that I found useful for me, and I suspect they would probably work for you as well. 37 Here is a typical de-essing command. ffmpeg -i inputfile.flac -filter_complex "deesser=i=0.5:m=0.5:f=0.5:s=o" -b:a 336k -sample_fmt s16 outputfile.flac 38 The important arguments are i, m, and f. i is intensity for triggering de-essing. The allowed range is 0 to 1. The default is 0. By experimentation I found that "0" means no de-essing, and "1" is maximum de-essing. I found that setting it to "0.5" gave satisfactory results. 39 m is the amount of "ducking on the treble part of sound". The allowed range is 0 to 1. The default is 0.5. By experimentation I found that "1" means no de-essing, and "0" is maximum de-essing. I found that setting it to "0.5" gave satisfactory results. 40 f is how much of the original frequency content to keep when de-essing. The allowed range is 0 to 1. The default is 0.5. By experimentation I found that "1" means no de-essing, and "0" is maximum de-essing. I found that setting it to "0.5" gave satisfactory results. 41 Setting "m" or "f" too high can result in a distorted output as too much of the original sound is cut out. The defaults of 0.5 in both cases gave audible improvements without noticeable distortion. 42 There is an additional parameter called "s". This controls whether the de-essing filter does anything. Setting it to "o" is the normal and default mode. Setting it to "e" causes it to output just the components that it would normally have filtered out. This is useful for testing purposes so you can see what and how much is being filtered. You only use this when experimenting with different values. Setting it to "i" causes the input to be passed through without de-essing. This would be useful in scripts where you want to use a variable to control whether or not to use the de-esser while still creating the expected output file. 43 There are two other elements of the command which were included but are not strictly speaking part of the de-essing filter itself . These are " -b:a 336k" and "-sample_fmt s16". " -b:a 336k" sets the audio bit rate to 336k. "-sample_fmt s16" sets the audio sample format to 16 bit. I found it necessary to specify these in order to prevent the de-essing filter from changing formats. They are not part of de-essing however. 44 De-Essing with Sox You can also de-ess with Sox. However, this is more complex for several reasons. One reason is that Sox does not have its own de-essing filters. Instead it uses optional plug-ins, and you must find and install these. The actual plug in may vary depending on what operating system you are using. The other reason is that it deals with the issue in fairly low level parameters, and so is a bit more complex to describe. Because of this I will skip over describing this in detail and just give a very brief overview. If anyone would like me to describe in more detail how to de-ess with Sox, then send in a comment and I will do a short episode on it later. 45 Sox De-Essing Overview To de-ess with Sox, you first need to install the plug-ins. On Linux, these will be the TAP ladspa plug-ins. TAP stands for "Tom's Audio Processing" plugins. ladspa stands for "Linux Audio Developer's Simple Plugin API" To install the TAP plugins on Ubuntu, using the following command. sudo apt install tap-plugins The plug-in we need is called "tap_deesser.so". 46 In order to use the plug-ins, you need to set the path as a variable. On Ubuntu this is. export LADSPA_PATH="/usr/lib/ladspa:" I put the above in the shell script which calls the Sox de-esser. 47 To use the Sox de-esser, you do the following: sox inputfile.flac outputfile.flac ladspa tap_deesser tap_deesser -30 4500 48 tap_deesser tap_deesser tells it which plugin to use. We need to state tap_deesser twice because the first is the name of the ".so" file and the second is the name of the plugin. A single "so" file can contain multiple filters, although in this case there is only one. -30 is the threshold in dB at which to start to apply the filter. 4500 is the frequency in Hz that the filter centres around. 49 The TAP web page has a table of recommended frequencies. These are: Male 'ess' 4500 Hz Male 'ssh' 3400 Hz Female 'ess' 6800 Hz Female 'ssh' 5100 Hz You will need to do some trial and error to find what works best for you. 50 De-Essing Summary De-essing can be used to make minor improvements to voice quality by reducing certain harsh sounds which may be exaggerated by a microphone. If it sounds like a lot of work you can probably simply not bother with it and not really miss it. -------------------- 51 Normalizing Normalizing a signal means adjusting it to meet a specified level. For audio it means adjusting the volume or sound level. You may wish to normalize the audio of your recording to make it easier to listen to when reviewing it. The copy that you send to HPR however should be the original un-normalized version. 52 Sound level is measured in two ways, dB and LUFS. The latter is a more sophisticated way of measuring things which takes into account how the human ear perceives loudness. I won't go into a lot of detail in that regards, other than to say that just accept LUFS as a unit of perceived loudness that is the international standard. LUFS stands for "Loudness Units referenced to Full Scale", and is part of the EBU R128 standard, where EBU stands for European Broadcast Union. In both cases the measured value is a negative number, with numbers smaller in magnitude being louder. Smaller in magnitude means closer to zero. 53 HPR will adjust the sound level for publication, but if you wish to check the audio before uploading it can help to adjust it to something close to what HPR will do so that you can listen to it at a volume which most listeners will hear. In my case full volume on the audio system input produced a sound level which was much lower than a typical HPR episode. However, the volume level in the flac file itself can be adjusted using ffmpeg. 54 Measuring Volume Level First we need to see what the volume level is for a typical HPR podcast. To do this we use ffmpeg. In this example we are using an episode named "hprpodcast.mp3". Pick an episode which you think is suitable and copy the file to the working directory. 55 In the following script we use a volumedetect filter. The text we want normally outputs to standard error, so we have to do a bit of bashery to redirect this to standard output so it will go through a pipe. We then grep for the string "I:". This will have the average volume level in "loudness units" (LUFS). Then we extract the number, giving us a target LUFS level. 56 ffmpeg -i hprpodcast.mp3 -filter:a ebur128=framelog=quiet -f null /dev/null 2>&1 | grep "I:" | cut -d: -f2 57 Unfortunately I can't find a Sox feature which handles EBU loudness, so we need to work in dB instead. Here is the sox version. However, note that this may not work on mp3s if sox mp3 handing is not installed. 58 sox hprpodcast.mp3 -n stats 2>&1 | grep "RMS lev dB" | rev | cut -d" " -f1 | rev 59 You can use either of these for measuring the volume or sound level of an audio file. However, note that individual episodes from HPR may vary a bit in terms of loudness. In the samples that I looked at, this however was less than 1 LUFS or dB while my own recording was roughly 5 LUFS lower in volume than a typical HPR episode. -------------------- 60 If you Google for the EBU R128 standard the AI result will confidently tell you to use a target of -23 LUFS. However, this is wrong, which shouldn't be of any surprise if you are familiar with using AI. 61 The -23 LUFS figure is for broadcast television. There is in fact no standard level for podcasts. However, there is apparently a general industry convention of using somewhere around -17 LUFS. If I look at the first two HPR episodes that I did, HPR normalized them to -16.8 LUFS and -17.8 LUFS, while the original FLAC files that I submitted were -21.6 LUFS and -22.3 LUFS respectively. 62 So HRP appear to be targeting somewhere around -17 LUFS as well. We will therefore use -17 LUFS as our target for our own copy for review. -------------------- 63 The nice thing about using the EBU filter in FFMPEG is that this is very simple. Here is the FFMPEG version. 64 ffmpeg -i inputfile.flac -af loudnorm=I=-17:TP=-2.0:LRA=7.0 -ar 44.1k outputfile.flac 65 "I" is the LUFS target. LRA is the loudness range target. The default value is 7.0 so I used that. TP sets the maximum true peak. The default value is -2.0. so I used that. -------------------- 66 With Sox things are a bit more difficult. There is no direct method of setting the loudness that I am aware of, so we need to measure the current sound level in dB, do some calculations, and then apply that as a gain factor to the output. 67 First we need to subtract the measured db level from our flac file from the target db level from the HPR episode we decided to use as a sample. Bash by itself normally just does integer math. However, we would like to have at least one decimal point of resolution to work with. The simple solution is to do this calculation using bc, the shell arbitrary precision calculator. 68 Then take this new value and use it in a "volume" filter. The number which we give sox is the amount to increase or decrease the volume by. Sox will then output a new file with the new volume level. You can now listen to this file under conditions more closely approximating what it will be like after HPR have done their own audio adjustments and normalizaton on it This helps when listening to the file for any problems before you upload it. 69 Rather than reading 5 lines of complex shell script to you, I will put a copy of it in the show notes. level=$( sox $inputfile -n stats 2>&1 | grep "RMS lev dB" ) leveldb=$( echo "$level" | rev | cut -d" " -f1 | rev ) targetdb="-18.9" volumechange=$(echo "scale=2 ; $targetdb - $leveldb" | bc ) sox $inputfile $outputname gain "$volumechange" -------------------- 70 Normalization should be the last thing you do to the file. It should be done after any noise filtering, such as low pass, high pass, bandreject, etc. If you normalize first, you will be amplifying the noise as well as the desired signal. 71 The exact normalization level used for review purposes doesn't matter, as HPR will apply their own later. All we are doing at this point is adjusting the volume to something which approximates a normal episode so you can listen to it for final review. 72 When you send your file to HPR, send the original *unnormalized* version, not the normalized version. When you normalize an audio signal, if you are not careful you may introduce things which cause problems with later additional processing. HPR probably do more things to the audio than just normalizing and so they need the unnormalized file so that they can do their own normalizing last. -------------------- 73 If at this point you are happy with the recording as is, you are ready to send the *unnormalized* version to HPR. The scripts to implement the features discussed in this episode will be in the show notes. 74 Conclusion In this episode we covered basic filtering using ffmpeg and sox. We discussed what noise was and some of the origins of noise. We talked about the audio frequency range and the limitations of common hardware used to record and listen to podcasts. We covered basic high and low pass filters used to limit the audio frequency range in order to remove possible low and high frequency noise. 75 We discussed specific filters to eliminate 50 and 60 Hz electrical power noise. We talked about de-essing, what it was, why you may wish to use it, and some basic de-essing filter implementation details. We discussed normalizing, what it is, why you may wish to use it, and how it relates to podcasting conventions. 76 In the next episode we will discuss analyzing audio signals to help find the sources of noise problems. We will also discuss creating filters to eliminate any problems that we found. In my case I had a problem with the microphone that I use, and I describe how I used filters to deal with that problem. 77 This has been the second episode in a four part series on simple podcasting. -------------------- EBU R128 Loudness Measurement using FFMPEG #!/bin/bash echo "EBU r128 loudness measurement using FFMPEG" for inputfile in *.flac *.mp3 ; do level=$( ffmpeg -i $inputfile -filter:a ebur128=framelog=quiet -f null /dev/null 2>&1 | grep "I:" | cut -d: -f2 ) echo $inputfile $level done -------------------- DB Sound Level Measurement using Sox #!/bin/bash # Sox version. May not work for mp3 if an mp3 format handling is not installed. echo "dB sound level measurement using Sox." for inputfile in *.flac *.mp3 ; do level=$( sox $inputfile -n stats 2>&1 | grep "RMS lev dB" ) leveldb=$( echo "$level" | rev | cut -d" " -f1 | rev ) echo $inputfile $leveldb done -------------------- EBU R128 Loudness Normalization using FFMPEG #!/bin/bash # Adjust the volume to a desired level. for inputfile in *.flac ; do j=$( basename $inputfile ".flac" ) outputname="$j""-normff.flac" ffmpeg -i $inputfile -af loudnorm=I=-17:TP=-2.0:LRA=4.0 -ar 44.1k $outputname echo $outputname done -------------------- DB Sound Level Normalization using Sox #!/bin/bash # Adjust the volume to a desired level. for inputfile in *.flac ; do j=$( basename $inputfile ".flac" ) outputname="$j""-normff.flac" # Measure the volume level and extract the mean volume. level=$( sox $inputfile -n stats 2>&1 | grep "RMS lev dB" ) leveldb=$( echo "$level" | rev | cut -d" " -f1 | rev ) # Calculate the difference in dB desired. Scale specifies the number of decimal places. # Target db is the volume measured on hpr4506 (UCSD-P-System). targetdb="-18.9" volumechange=$(echo "scale=2 ; $targetdb - $leveldb" | bc ) echo "Using sox: File: $inputfile Original level: $leveldb Change by: $volumechange" # Adjust the volume. sox $inputfile $outputname gain "$volumechange" done -------------------- Full processing pipeline for making simple podcasts using FFMPEG #!/bin/bash #!/bin/bash # Full processing pipeline for making simple podcasts. # ====================================================================== # Concatenate multiple flac files into a single flac file. # This is used to combine podcast recorded segments into a single # flac file for uploading to HPR. concataudio () { outputname="$1" # First create the list file. printf "file '%s'n" [0-9][0-9].flac > podseglist.txt # Now concatenate them ffmpeg -f concat -safe 0 -i podseglist.txt "$outputname" rm podseglist.txt } # ====================================================================== # Basic filters. filter () { inputfile=$1 outputname=$2 # Using ffmpeg. # The high and low pass filters. hlpfil="highpass=f=80, lowpass=f=12000" # Band reject filters filter for 60Hz and another for 50Hz. linefil="bandreject=f=60:width_type=h:w=20, bandreject=f=50:width_type=h:w=20" # Using ffmpeg ffmpeg -i $inputfile -af "$hlpfil, $linefil" $outputname } # ====================================================================== # De-Essing. deessing () { inputfile=$1 outputname=$2 option=$3 # De-essing filter. ffmpeg -i $inputfile -filter_complex "deesser=i=0.5:m=0.5:f=0.5:s=$option" -b:a 336k -sample_fmt s16 $outputname } # ====================================================================== # Normalizing the audio to EBU R128 standard for review using ffmpeg. normffmpeg () { inputfile=$1 outputname=$2 # Normalize to EBU R128 standard. ffmpeg -i $inputfile -af loudnorm=I=-17:TP=-2.0:LRA=4.0 -ar 44.1k $outputname } # ====================================================================== # Output an MP3 version to help with reviewing. mp3convert () { inputfile=$1 # Get the name of the file and then create the output file name. j=$( basename $inputfile ".flac" ) outputname="$j"".mp3" # Convert to MP3. ffmpeg -i $inputfile $outputname } # ====================================================================== # Concatenate the separate audio files. concataudio fullpod-unfiltered.flac # Basic filtering. filter fullpod-unfiltered.flac filtered.flac # De-essing. This is the version to send for publishing. # The third argument should be "o" for de-essing, or "i" for pass through without de-essing. deessing filtered.flac fullpod.flac o # Normalized for review. normffmpeg fullpod.flac fullpod-norm.flac # Output an MP3 copy for review. mp3convert fullpod-norm.flac -------------------- -------------------- Provide feedback on this episode.

Did you like the episode? Send me a text and let me know!!How to Win at AI Search: The Complete Audience Building Blueprint for CreatorsStop shouting into the digital void. In this episode, we break down the exact system for building an audience using AI search optimization, TikTok SEO, and podcast growth strategy — starting from absolute zero.What You'll Learn:Why "build it and they will come" is killing your growthHow AI tools like ChatGPT, Perplexity, and Google AI Overviews are the new gatekeepersThe exact formula to get your content cited as the definitive sourceTimestamps:[00:00] The biggest lie in the creator economy [02:00] How the internet shifted from phone book to AI matchmaker [03:00] What is Assistive Agent Optimization (AAO)? [04:00] The Algorithmic Trinity: LLMs, traditional search, and knowledge graphs explained [05:00] The Zero Sum Moment: why AI search converts 4.4x better than traditional search [06:00] How to build Entity Authority with ChatGPT and Perplexity [07:00] Zero click search: threat or opportunity for creators? [09:00] TikTok SEO: the 4 ranking factors that drive views for years [11:00] The TSAK Formula: how to name your podcast for AI search discovery [13:00] The psychology of virality: 4 ingredients that drive shares [15:00] Attraction vs Retention: Spotify's brutal benchmark metrics explained [17:00] How to build an audience from zero followers using borrowed audiences [19:00] LUFS standards for Apple Podcasts and Spotify — and why audio quality destroys retention [21:00] The future: when AI agents make buying decisions without humansKey Concepts Covered: Assistive Agent Optimization · Algorithmic Trinity · Entity Authority · Zero Sum Moment · TSAK Formula · TikTok SEO · Spotify Retention Benchmarks · LUFS Audio Standards · Social Currency · BorrowedDo you want to know what is your worst Hurdle is so you know what you want to do first to get across the start line?? Go to tuepodcast.net/quiz to get your 3 minute assessment right now and find out what your most prevalent hurdle is and how to start to overcome it!tuepodcast.net/quiz For a 15% discount on your first purchase go RYZEsuoerfoods.com use code PODNA15 Thank you for being a Skoobeliever!! If you have questions about the show or you want to be a guest please contact me at one of these social mediasTwitter......... ..@djskoob2021 Facebook.........Facebook.com/skoobamiInstagram.....  instagram.com/uepodcast2021tiktok....... @djskoob2021Email............... Uepodcast2021@gmail.comSkoob at Gettin' Basted Facebook PageAcross The Start Line Facebook CommunityFind out what one of the four hurdles of stop is affecting you the most!!Black Friday coaching Sale now!! 65% off original price! go to stan.store/skoob to book your appointment and take advantage of this limited time offer! On Twitter @doittodaycoachdoingittodaycoaching@gmailcom

This week's episode is about the artisanship and business of podcast editing with veteran editor Bryan Entzminger. Bryan shares how he began podcasting in 2014, inspired by John Lee Dumas' formulaic approach to Entrepreneurs On Fire and how that era of entrepreneurship-focused shows shaped early podcasting.We unpack why many podcasts have naturally “run their course” after five or six years, how the pandemic boom has cooled, and why Bryan has deliberately avoided rebuilding a client roster too concentrated on a few shows.On the technical side, Bryan walks through his journey from GarageBand to Twisted Wave and finally to Hindenburg, which he still considers the most intuitive for voice-first production and detailed technical edits. He contrasts this with transcript-based tools like Descript, explaining why automated “de-umming” often destroys pacing, breath, and emotion, and still requires hours of cleanup by a human editor.Bryan also shares favorite repair tools (like DX Revive, Supertone Clear, and iZotope), his go-to MP3 specs and -16 LUFS target, and thoughts on Zoom audio, video podcast distribution via RSS, and experiments with platforms like Fountain, TrueFans, and his own testbed show, Bad Podcast Pitches.Please sign up for the SOUNDING OFF Newsletter. All the things that went unsaid on the show.Also we added the Sound Off Podcast to the The Open Podcast Prefix Project (OP3) A free and open-source podcast prefix analytics service committed to open data and listener privacy. You can be a nosey parker by checking out our downloads here.Thanks to the following organizations for supporting the show:Megatrax - Licensed Music for your radio station or podcast production company.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In Episode 360 of the Mixing Music Podcast, hosts Dee Kei and Lu get technical and go deep on loudness, gain staging, and headroom. Dee Kei shares a concern he has been noticing in his own work: he may be leaving loudness on the table over the length of a full song, not just in short-term LUFS moments. They explore the balance between beauty and accessibility, and how loudness is not just a mastering issue, it is a creative dimension that shapes emotion, energy, and perceived impact.The conversation breaks down what gain staging really means in modern mixing, why many classic rules were originally about managing noise, and how today it is more about preserving freedom and control in your workflow. They talk about practical target levels engineers use when hitting plugins, why some mixes feel boxed in when the mix bus is too hot, and how dense sessions can build level fast even when individual tracks are not peaking high.They also zoom out into how loudness decisions translate across mediums. Dee Kei shares how Dolby Atmos delivery constraints can change the feel of a record that relies on aggressive loudness for momentum, and how downmixes can shift balance depending on playback. They touch on the psychological reality that slightly louder often gets perceived as better, even when nothing else changes, and why sending a limited version of your mix to a client can matter for perception and decision-making.To close, they share real-world stories about how playback environments like movie theaters or broadcast chains can unintentionally slam your audio through limiters, plus a reminder that “loud and good” is not a contradiction. This episode is for mixers who want a clearer mental model of loudness, headroom, and gain staging, and how to make those decisions intentionally without destroying the mix.Support this podcast at — https://redcircle.com/mixing-music-music-production-audio-engineering-and-music/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

 RTL Pro Tools Masterclass: 8 Spuren, Werbeblöcke & die Hölle der LUFS-Norm! Nach der Demonstration des Remote-Workflows tauchen Jan und Gerd in das Herz der Postproduktion ein: Pro Tools. Sie erklären, wie die Projekte strukturiert sind, welche 8 diskreten Audiospuren (O-Töne, Atmo, Musik, Sprecher/Rohsprecher) der RTL-Standard erfordert und wie die Signalwege (inklusive Director-Signal) zentral in der Hauptsoftware verwaltet werden. Auf YouTube sehen: https://youtu.be/ondfjo_0Ihs?si=tZpAuy8EWrUe4QDz   Zum kostenlosen Cubase-Stammtisch anmelden: subscribepage.io/1D69jt Wenn ich Dir helfen konnte, freue ich mich über einen virtuellen Kaffee ;-) https://ko-fi.com/timheinrich Orchestra Guide - Perfekte Orchester-Mockup-Balance: https://payhip.com/b/oRXKh Hier das Episoden-Archiv als PDF runterladen: https://www.sounth.de/media/podcast/sounTHcast.pdf Facebook-Gruppe: https://www.facebook.com/groups/309751689699537/ Fragen und Anregungen an sounthcast@sounth.de Website Tim Heinrich: https://sounth.de  

Ich zeige dir, wie du in Cubase die Lautheit (LUFS) korrekt messen kannst – ein wichtiger Schritt für saubere Mixe, Mastering und Streaming-Plattformen wie YouTube oder Spotify. Neben der grundlegenden Messung der Lautheit bekommst du außerdem praktische Tipps, mit denen du deutlich schneller zum Ziel kommst. Ich zeige dir unter anderem, wie du Audio-Files direkt auf eine gewünschte Lautheit normalisieren kannst und worauf du dabei achten solltest. Auf YouTube sehen: https://youtu.be/SGypwCbqqoI   Zum kostenlosen Cubase-Stammtisch anmelden: subscribepage.io/1D69jt Wenn ich Dir helfen konnte, freue ich mich über einen virtuellen Kaffee ;-) https://ko-fi.com/timheinrich Orchestra Guide - Perfekte Orchester-Mockup-Balance: https://payhip.com/b/oRXKh Hier das Episoden-Archiv als PDF runterladen: https://www.sounth.de/media/podcast/sounTHcast.pdf Facebook-Gruppe: https://www.facebook.com/groups/309751689699537/ Fragen und Anregungen an sounthcast@sounth.de Website Tim Heinrich: https://sounth.de  

JOIN OUR PATREON AND GET ACCESS TO EXCLUSIVE CONTENT: https://mixingmusicpodcast.com/exclusiveI WRITE BOOKS FOR CHILDREN: https://deekeiandkayoko.comHIRE DEE KEI: links.deekeimixes.comHIRE LU: https://soundbetter.com/profiles/1419...Hire James: https://www.jamesparrishmixes.com/Find Dee Kei and Lu on Social Media:Instagram: @DeeKeiMixes @masteredbyluTwitter: @DeeKeiMixes @masteredbyluJoin the ‘Mixing Music Podcast' Discord:   / discord  The Mixing Music Podcast is sponsored by ⁠Izotope⁠, ⁠Antares (Auto Tune)⁠, Sweetwater, ⁠Plugin Boutique⁠, ⁠Lauten Audio⁠, ⁠Filepass⁠, & ⁠Canva⁠The Mixing Music Podcast is a video and audio series on the art of music production and post-production. Dee Kei, Lu, and James are professionals in the Los Angeles music industry having worked with names like Odetari, 6arelyhuman, Trey Songz, Keyshia Cole, Benny the Butcher, carolesdaughter, Crying City, Daphne Loves Derby, Natalie Jane, charlieonnafriday, bludnymph, Lay Bankz, Rico Nasty, Ayesha Erotica, ATEEZ, Dizzy Wright, Kanye West, Blackway, The Game, Dylan Espeseth, Tara Yummy, Asteria, Kets4eki, Shaquille O'Neal, Republic Records, Interscope Records, Arista Records, Position Music, Capital Records, Mercury Records, Universal Music Group, apg, Hive Music, Sony Music, and many others.This podcast is meant to be used for educational purposes only. This show was filmed and recorded at Dee Kei's private studio in North Hollywood, California. If you would like to sponsor the show, please email us at mixingmusicpodcast@gmail.com.Support this podcast at — https://redcircle.com/mixing-music-music-production-audio-engineering-and-music/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

In this solo episode of the Mixing Music Podcast, Dee Kei breaks down why loudness is part of the mix, not an afterthought for mastering. He explains why you should never send clients an unlimited mix as the main reference, even if a separate mastering engineer is involved, and why your job as a mixer is to deliver something that already feels like a finished record.DK talks through how to limit with intention, using loudness as an emotional dimension of the record instead of just cranking LUFS. He gets into the illusion of dynamics, sidechain feel, clipping, multiple limiters, and how different genres (jazz versus hyper-loud pop) demand different loudness philosophies. He also explains why mastering engineers would rather receive a well-intentioned, fully limited mix than an unintentional “safe” premaster that forces them to guess what you wanted.To close, DK rants a bit about Dolby Atmos and spatial audio: how strict loudness specs in Atmos remove an entire emotional dimension for music, why it makes sense for film and games, and why spatial formats are pushed in music mostly to sell headphones, not to make records sound better.What you will learn:Why you should always send clients a limited mix or “reference master”How to think about loudness as part of tone, impact, and emotionIllusion of dynamics, sidechain feel, clipping, and multi-stage limiting in loud mixesHow intentional rough mixes and stems reduce guesswork for mixers and mastering engineersWhy Atmos loudness standards remove a creative dimension for music and are better suited for film and TV SUBSCRIBE TO OUR PATREON FOR EXCLUSIVE CONTENT!⁠SUBSCRIBE TO YOUTUBE⁠Join the ‘Mixing Music Podcast' Discord!HIRE DEE KEIHIRE LU⁠HIRE JAMES⁠Find Dee Kei and Lu on Social Media:Instagram: @DeeKeiMixes @MasteredbyLu @JamesParrishMixesTwitter: @DeeKeiMixes @MasteredbyLuThe Mixing Music Podcast is sponsored by ⁠Izotope⁠, ⁠Antares (Auto Tune)⁠, Sweetwater, ⁠Plugin Boutique⁠, ⁠Lauten Audio⁠, ⁠Filepass⁠, & ⁠Canva⁠The Mixing Music Podcast is a video and audio series on the art of music production and post-production. Dee Kei, Lu, and James are professionals in the Los Angeles music industry having worked with names like Odetari, 6arelyhuman, Trey Songz, Keyshia Cole, Benny the Butcher, carolesdaughter, Crying City, Daphne Loves Derby, Natalie Jane, charlieonnafriday, bludnymph, Lay Bankz, Rico Nasty, Ayesha Erotica, ATEEZ, Dizzy Wright, Kanye West, Blackway, The Game, Dylan Espeseth, Tara Yummy, Asteria, Kets4eki, Shaquille O'Neal, Republic Records, Interscope Records, Arista Records, Position Music, Capital Records, Mercury Records, Universal Music Group, apg, Hive Music, Sony Music, and many others.This podcast is meant to be used for educational purposes only. This show is filmed and recorded at Dee Kei's private studio in North Hollywood, California. If you would like to sponsor the show, please email us at ⁠deekeimixes@gmail.com⁠.Our Sponsors:* Check out Uncommon Goods: https://uncommongoods.com/mmpodSupport this podcast at — https://redcircle.com/mixing-music-music-production-audio-engineering-and-music/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Ready to trade plugin FOMO and meter anxiety for moves that actually make your tracks better? Marc pulls seven stand-out moments from a huge year of conversations with producers, engineers and artists to help you finish faster, mix with confidence and stay creatively sharp.We kick off with a surprising angle on depth: shaping contrast with bit depth instead of defaulting to saturation. You'll hear how assigning different resolutions to drums, pads, and leads can create three-dimensional mixes that hold up in mono and stereo. From there,  dismantle the gear trap. Modern DAWs already include the essentials; the real upgrade is mastering fundamentals like tonal balance, gain staging and arrangement so every later purchase has purpose.Loudness gets a refresh with a simple truth: LUFS is the result of mastering, not the target. Focus on tone, punch and cohesion, then check integrated LUFS for how platforms will treat your music. We lean into ear-first decisions, too—set a solid static mix, push the faders, and don't let a scary-looking EQ curve talk you out of the right move. On the mastering front, we explore why a dedicated mastering engineer is often the first truly fresh set of ears your project gets, and how that perspective helps you avoid circular tweaks and ensures reliable translation.Songwriting fans get a creative jolt as we talk about lyrics as well-narrated hallucinations grounded in truth. Wait for ideas that feel necessary, then go all in. Finally, we round things out with workflow wisdom: reference tracks, clear sound selection, minimal EQ, and fader-first mixing to keep momentum high and second-guessing low. If you want practical, repeatable steps that improve your music across streaming, clubs and headphones, this highlight reel delivers.Links mentioned in this episode:Listen to E179Listen to E199Listen to E186Listen to E197Listen to E193Listen to E187Listen to E213Send me a message Support the showWays to connect with Marc: Listener Feedback Survey - tell me what YOU want in 2026 Radio-ready mixes start here - get the FREE weekly tips Book your FREE Music Breakthrough Strategy Call Follow Marc's Socials: Instagram | YouTube | Synth Music Mastering Thanks for listening!! Try Riverside for FREE

This week on the New Music Business podcast, Ari sits down with Daniel Rowland, a rare combination of audio engineer, producer, tech executive, and educator. The music he's worked on has amassed over 15 billion streams, earning Emmy and Oscar wins, Grammy nominations, and multiple platinum certifications. His credits span an eclectic range of artists and projects, including Nina Simone, Nine Inch Nails, Thundercat, Star Wars, Pixar, and John Wick. Beyond the studio, Daniel serves as VP of Strategy and Partnerships at LANDR Audio and a longtime professor at MTSU, where he champions ethical, AI-driven tools that empower creators.In this episode, Ari and Daniel explore the ever-evolving intersection of music, technology, and creativity. They dive deep into the art and science of mastering for different formats, the rise of AI in music production, and how to maintain authenticity in an increasingly automated world. Daniel also shares his personal journey from musician to educator and innovator, offering invaluable insights for artists navigating the future of sound and creation.https://www.linkedin.com/in/rowlanddanielhttps://www.landr.com/00:00 – Mixing vs. Mastering & the Role of AI in Quality Control02:30 – Introducing Daniel Rowland & His Career Overview06:45 – Daniel's Journey from Musician to Engineer & Educator09:45 – Dolby Atmos & Spatial Audio: Fad or Future?19:00 – What Mastering Really Is (and Isn't)26:00 – The “Curmudgeon” Era of Mastering Engineers29:00 – Loudness, LUFS, and the Streaming Revolution36:45 – The Vinyl Comeback: Problems and Pitfalls47:45 – Automated Mastering & LANDR's Evolution54:00 – The Future of AI and Creativity in MusicEdited and mixed by Ari DavidsMusic by Brassroots DistrictProduced by the team at Ari's TakeOrder the THIRD EDITION of How to Make It in the New Music Business: https://book.aristake.com Hosted on Acast. See acast.com/privacy for more information.

Sappalot schon 21 Uhr und keine Sau hat den Ankündigungstext geschrieben. Bleibt also AUCH DAS an mir hängen. Danke Merkel. Danke Schmitti. Danke Klaas. Danke Jakob. Also die Folge „Ist der Prominente flutschig?“ weist einen sauberen Headroom von etwa -3 dBFS auf, keine Clippings. Der Dynamikumfang liegt bei rund 14 LUFs, was diese Folge sehr natürlich wirken lässt. Das untere Mittenband um 250 Hz ist leicht überbetont, wodurch die Mischung etwas mulmig wirkt. Die Höhen oberhalb 10 kHz sind klar, aber minimal harsch bei Sibilanten. Das Stereobild ist gut definiert, mit stabiler Phantommitte. Der Raumanteil ist angenehm dezent, könnte aber etwas mehr Tiefe vertragen. Ansonsten ist Schmitti wohl wie ein Allwetterreifen. Klaas besucht bald einen Clownerie Workshop und Jakob erklärt warum manche Promis erst flutschig gemacht werden, obwohl sie sich aber ca. 8 min später wieder zu verpissen haben. Also hört diese Folge „Baywatch Berlin“. Ich kann nur sagen auch wenn die Hauptmikrofonierung etwas zu nah wirkt, bleibt das Rauschniveau aber unauffällig. Euer Tonmann Pfeife. Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte: https://linktr.ee/BaywatchBerlin

Hang with Chris, Rob and John as they get excited for fall sports, recount their weekend activities and debate smoking alternatives that they've tried. Also, another good show bites the dust, Chris geeks out, they discuss the future of Ai in finance and look at which jobs are the safest, and which are most susceptible to being replaced by Ai.   Enjoy!

In this episode, Dee Kei and Lu dive deep into the sonic and philosophical chaos of mixing hyperpop, sigilkore, and other maximalist genres — where distortion, clipping, and sheer loudness aren't flaws, they're features.From pushing mixes to -2 LUFS to layering clippers at every stage, Dee Kei shares his firsthand experiences mixing records for artists like Odetari and Barelyhuman, and how that work has redefined his approach to both mixing and creativity.But this episode goes beyond technique. It explores the philosophy behind weirdness, the cultural value of outsider genres, and why trying to normalize everything can kill innovation. We talk gear (StandardClip, Cake, Gold Clip), philosophy (Zen, dualism, the Just World Fallacy), and how freedom from expectation leads to better art.

Multi-user has rolled out! No more sharing your password with people that help you podcast! And it's totally easy to add them to your account! Insight into measuring digital audio in media mix models, tools to figure out our LUFS --- IYKYK, a workflow for getting control of channels in Apple Podcasts, discussing the Cumulus Media & Signal Hill Insights' Podcast Download Spring 2025 Report, how to download all of your files from an RSS feed? We got answers! Plus, geographic and user agents! Audience feedback drives the show. We'd love for you to contact us and keep the conversation going! Email thefeed@libsyn.com, call 412-573-1934 or leave us a message on Speakpipe! We'd love to hear from you!

Multi-user has rolled out! No more sharing your password with people that help you podcast! And it's totally easy to add them to your account! Insight into measuring digital audio in media mix models, tools to figure out our LUFS --- IYKYK, a workflow for getting control of channels in Apple Podcasts, discussing the Cumulus Media & Signal Hill Insights' Podcast Download Spring 2025 Report, how to download all of your files from an RSS feed? We got answers! Plus, geographic and user agents! Audience feedback drives the show. We'd love for you to contact us and keep the conversation going! Email thefeed@libsyn.com, call 412-573-1934 or leave us a message on Speakpipe! We'd love to hear from you!

This week on The Pro Audio Suite, we're diving deep into a topic that sparks endless debate (and confusion) in VO forums everywhere: the noise floor. What does "-74 dB" even mean if you don't know how it was measured? And why are voice actors obsessing over numbers that might not matter? Join AP, Robbo, George The Tech, and Robert Marshall as they break down: What noise floor really is (and what it isn't) Why context, weighting, and window size matter RMS vs Peak vs LUFS – and why it all depends How your mic, preamp, plugins, and power can mess with your readings Whether your studio noise is actually a problem or just sounds nice We even talk about whether it's OK to use a high-pass filter, what settings to use, and why some mics (looking at you, TLM 103) are just better at hearing ghosts. If you've ever been told “your studio needs to be -60 dB or else,” this episode will save you some sleepless nights.

The Podcast Super Friends discussed the importance of and evaluating podcasts. We focused on technical quality, content, and promotion. Johnny got us started with microphones, managing echo, and maintaining consistent loudness levels (LUFS -15). For video, using high-quality cameras and proper lighting was emphasized. Jag shifted us over to content, stressing the importance of engaging introductions, avoiding overly long episodes, and asking hard questions respectfully. The friends were complimentary of Valerie Geller's recent appearacne on the Sound Off Podcast with Matt Cundill, where she spoke about her book and offered up OODLES of excellent podcast related tactcis on creative content. YOU CAN LISTEN TO IT HERE. Matt is all about the packaging of a show; including the artwork, metadata and how you show looks and appears in places like Apple, Spotify and Amazon. Catherine got us going on promotion strategies like sharing episode links, using visual cues on YouTube, and leveraging guest networks. The conversation also touched on the importance of making podcasts about the audience and being involved in relevant communities. Catherine also suggests using those subtle warm calls to engage existing customers or fans when promoting a podcast, especially for branded podcasts with built-in audiences. We missed David Yas on the show today but Johnny managed to promote his show called Past 10's Top 10 Time Machine. We are 5 Podcast Producers who make podcasts for People and Companies. Reach out to any one of us if you would like a show. Johnny Peterson - Johnny Podcasts https://www.johnnypodcasts.com Catherine O'Brien - Branch Out Programs  https://www.branchoutprograms.com/  Jon Gay: Jag in Detroit https://www.jagindetroit.com David Yas: Pod 617 - The Boston Podcast Network https://www.pod617.com/ Matt Cundill - The Sound Off Media Company https://soundoff.network Learn more about your ad choices. Visit megaphone.fm/adchoices

1158: En este episodio te hablo de dos procesos esenciales en la edición de audio: la normalización y la limitación. Ambos tienen que ver con el volumen, pero cumplen funciones diferentes y complementarias. Mientras que la normalización ajusta el nivel general de sonido para mantenerlo estable y evitar fluctuaciones incómodas, la limitación se encarga de recortar los picos que podrían sobresalir de manera molesta llegando incluso a distorsionar el audio de tu episodio. Al normalizar, puedes asegurarte de que todo suene con una coherencia que facilite la escucha.Esto significa que no tendrás que subir o bajar el volumen constantemente mientras reproduces tu podcast. La normalización por picos, por ejemplo, ajusta los niveles para que el punto más alto del audio no supere un valor establecido. Por otro lado, la normalización por sonoridad, utilizando estándares como los LUFS, se centra en cómo perciben los oyentes el volumen general, lo que la hace especialmente útil para mantener la consistencia entre episodios._________________'Clásicos con altura', el podcast donde redescubrirás la literatura universal desde una perspectiva contemporánea, patrocina este capítulo. Suscríbete a este proyecto de ALMA producido por Ekos Media en tu plataforma de podcast favorita:Youtube: https://www.youtube.com/playlist?list=PLawt9jac73-TsyWAHm063PZ-QWrQJJgWcSpotify: https://open.spotify.com/show/4HDVxSoYpMeJI3bPduHDxGPodlink: https://pod.link/1786617351_________________ La limitación, en cambio, se utiliza cuando un sonido en particular—como una carcajada fuerte o una tos frente al micrófono—genera un pico indeseado. El limitador actúa como una barrera que corta esos excesos de volumen, dejando fuera todo lo que sobrepasa un umbral específico. Aunque este proceso puede ser más agresivo, es ideal para evitar saturaciones y garantizar que ninguna parte del audio destaque de forma abrupta.En mi flujo de trabajo, suelo aplicar primero una normalización básica para nivelar el audio. Luego uso un limitador para eliminar cualquier pico que pueda estropear la grabación. Finalmente, cuando tengo todas las pistas montadas, aplico una normalización por LUFS para que el resultado final tenga un volumen uniforme. Este proceso me ha ayudado a crear episodios más equilibrados y agradables para los oyentes.Conocer estas herramientas y sus aplicaciones te permitirá entregar un producto más profesional y agradable de escuchar. De todas formas, no te fies de mi experiencia porque ni yo mismo lo hago siempre igual. Te invito a experimentar y encontrar la combinación que mejor se adapte a tu estilo y al tipo de contenido que produces.Si necesitas ayuda para mejorar la calidad de tus episodios o deseas profundizar más en estos conceptos, no dudes en contactarme. Estoy aquí para ayudarte a sacar el máximo partido a tu podcast. Puedes hacerlo a través del correo jorgemarin@eove.es¿Qué son los LUFS y cuál es el nivel idóneo para un podcast?https://alotroladodelmicrofono.com/que-son-los-lufs-y-cual-es-el-nivel-idoneo-para-un-podcast/_________________Consigue tu entrada para el directo de 'El Recuento Musical' el 10 de mayo en las Podnights Madrid a través de Eventbrite https://www.eventbrite.es/e/entradas-el-recuento-musical-en-podnight-madrid-1320889328539_________________¡Gracias por pasarte 'Al otro lado del micrófono' un día más para seguir aprendiendo sobre podcasting!Si quieres descubrir cómo puedes unirte a la comunidad o a los diferentes canales donde está presente este podcast, te invito a visitar https://alotroladodelmicrofono.com/unetePor otro lado, puedes suscribirte a la versión compacta, sin publicidad y anticipada de este podcast, 'El destilado del micrófono' a través de la plataforma Mumbler a través de: https://alotroladodelmicrofono.com/destilado (Puedes escucharlo en cualquier app de podcast mediante un feed exclusivo para ti).Además, puedes apoyar el proyecto mediante un pequeño impulso mensual, desde un granito de café mensual hasta un brunch digital. Descubre las diferentes opciones entrando en: https://alotroladodelmicrofono.com/cafe. También puedes apoyar el proyecto a través de tus compras en Amazon mediante mi enlace de afiliados https://alotroladodelmicrofono.com/amazonLa voz que puedes escuchar en la intro del podcast es de Juan Navarro Torelló (PoniendoVoces) y el diseño visual es de Antonio Poveda. La dirección, grabación y locución corre a cargo de Jorge Marín. La sintonía que puedes escuchar en cada capítulo ha sido creada por Jason Show y se titula: 2 Above Zero.'Al otro lado del micrófono' es una creación de EOVE Productora.

Send me a messageIn this episode of Inside The Mix, mastering engineer Ian Shepherd demystifies loudness metrics and debunks common mastering misconceptions while offering practical advice for producers looking to improve their masters without chasing arbitrary targets. Ian explains the role of LUFS in mastering, why normalization matters, and how focusing on musicality and dynamics leads to better results than simply hitting a loudness target.What You'll Learn: • LUFS measurements explained – momentary, short-term, and integrated loudness • How many LUFS should my master be? Understanding the balance between dynamics and loudness • The truth about LUFS for Spotify – why 83% of users never change loudness normalization settings • What does LUFS stand for? And why it's just one piece of the mastering puzzle • How normalization impacts your music across different streaming platforms • The role of audio normalization in creating a consistent listening experience • Why AI mastering struggles to match the emotional intent of human engineers • Spotify's approach to loudness and what it means for your masters • Internal dynamics – how balancing different sections of your song enhances clarity and impact • The mastering feedback loop – why collaboration between engineers and artists is keyIf you want your music to stand out in today's Spotify-dominated landscape, don't obsess over loudness numbers. Instead, focus on musicality, dynamics, and emotional impact. Test how your tracks sound at normalized streaming levels, and let the music, not the meters, drive your mastering decisions.Links mentioned in this episode:Listen to the Mastering Show PodcastMastering Engineer Reacts to 8 Pro Mixes of the Same SongHigher LUFS - do they REALLY always sound louder?YouTube ruined all my videos - UNLESS you disable this setting...Loudness Penalty: AnalyzePerception AB Support the showBook a FREE 20-minute Producer Potential Discovery Call Follow Marc Matthews' Socials:Instagram | YouTube | Synth Music Mastering Thanks for listening!!

As we bid farewell to 2024, this special episode of Inside The Mix celebrates your incredible musical wins while providing actionable tips to elevate your music production journey. My co-host, Aisle9, and I reflect on the year's highlights, share listener achievements, and answer your burning questions about mixing, mastering, and making meaningful progress as a musician.Whether you're wondering what is gain staging, how to get rid of mud in a mix, or how many reverbs to use in a mix, this episode is packed with practical advice and inspiration to kickstart your 2025 music goals.What We Cover:Celebrating inspiring listener wins, from mastering collaborations to personal breakthroughs.Breaking down the essentials of what is gain staging and its impact on sound quality.Exploring what is the best LUFS for mastering to meet industry standards.Proven strategies on how to get rid of mud in a mix for clarity and balance.Tips on how many reverbs to use in a mix for depth without over-complication.The importance of community and networking for creative growth.Setting achievable music goals for the upcoming year.Join us in reflecting on the power of collaboration, community, and creativity as we step into the new year with renewed energy and inspiration!Got feedback? I'd love to hear from you! Click here to leave a review, share your social media handles or website, and get featured in a future episode.Plus, one lucky reviewer will win a Starbucks voucher each month! Support the showWanna release more music in just 28 days? Click here Follow Marc Matthew's Socials:Instagram | YouTube | Synth Music Mastering

Endlich bekommen wir mal einen Blick hinter die Kulissen einer Podcastproduktion: Die Menschen, das Drama, die Technik und noch viel viel viel viel mehrVielen Dank an Tobias, Max, hand und Svenja für das Intro!Hier findest du alle Infos und Rabatte unserer Werbepartner: linktr.ee/daspodcastufo Hosted on Acast. See acast.com/privacy for more information.

On this Reaproducer episode of Audio Pizza, I will go over the basic workflow I use when recording and editing a podcast. Recording Typically, we use Zoom to connect with each other but then make our own local recordings of our ends. This way, we have good, pristine copies ready for editing. On the Mac, I use Audio Hijack or Reaper to do the recording. Once I have each of the individual files from the participants, I rename them with short filenames to cut down screen reader verbosity when navigating the project. I save a Reaper project and paste the files straight into the blank project, ready for editing. Project Settings To make navigating the audio easier for this type of editing, in the View menu, I change the Time unit for ruler to Minutes and Seconds, and Press Option+Return to access project settings, to adjust the tempo to 60, and time signature to 60 over 4. This may not make a lot of sense musically; however, it is useful for navigating by minutes with Page Up and Page Down, and seconds with Command+Page Up and Page Down. These can then be set as default project settings if you wish and it will also then retain your choice for the time ruler. Normalising After pasting the files into their own tracks in the project, I select all the items by first selecting one item, and then extending the selection with Command+A to all the items. I use Shift+U to bring up the SWS/BR - Normalize loudness dialog. This gives me the option to normalise each item to my preferred starting value of –23 LUFS. It is a good idea to add a limiter to your master track at this point, as it's possible that some parts of the recording will be coming in above 0 dB and clipping. Item: Auto trim/split items (remove silence) Control+Accent will bring up this dialog. As the name suggests, it works on the items rather than the tracks and will split each selected item based upon the content of the item. Where the level is above the set threshold, in this case, where the particular person is talking, it will leave an item, and anywhere where the level falls below the threshold, it will remove an item. So in those locations on the track where the level was below, the track will be empty. In effect, if set correctly, it will split before and after each passage of speech and remove the rest. This has some significant benefits and can speed up the editing process. You can now quickly navigate to particular parts of the audio and make the desired change on that item. This might be adjusting the volume of a phrase, removing a section, or shifting around the content of the podcast. However, it's important that your original files have a nice low noise floor, as when the quiet sections are removed, there is complete silence on that track. This can be disconcerting for the listener if there's too much noise in the sections when the person is speaking and this cuts in and out. Basic Editing You can navigate to an item to be removed with Command+Left/Right Arrows, and delete, or cut and paste it to another location with Command+X and Command+V. Keep in mind your current ripple state: off, or all tracks. Usually, you would not want to use the ripple per track option as you are likely to make subsequent content go out of sync. If a particular item is too loud or too quiet, it can be quickly adjusted with Command+Up/Down Arrow. This will adjust the particular take within the item by 1 dB. Trimming Left or Right Edge of Item to Edit Cursor One quick way of cutting off the end of an item is to use the action found on Control+Shift+Period — Item edit: Trim right edge of item to edit cursor. Typically, you could split the end of the item off, select that item, and delete it. However, if you place your cursor in the same location you would have made the split, and trim the right edge to that point, you've accomplished the same thing with one keystroke. This can be used to extend an item as well. Place the edit cursor after the selected item, and again hit the same command, and it will grow back that edge to this point. Of course, the same works for the beginning of an item, using the Trim left edge to edit cursor command. You can either cut the beginning off or grow it back depending upon where you place your cursor compared to the selected item. Another benefit of using this method is that it does not matter how you have ripple set. Had you used the split and delete method, it would be prudent to first check your ripple state to avoid unintended consequences. Custom Actions If you find yourself performing sequences of actions commonly, you may wish to create custom actions that will allow you to perform the actions with one keystroke. I found that I was often setting the end of a time selection, jumping back a bit and then playing, skipping the time selection to preview the edit. This is one of the custom actions I demo along with a couple more. To create a custom action, go into the actions list with F4, click the New action button, then select New custom action. You are then able to add a series of actions to create your own custom action. If you have actions in the wrong order, you can move them in the list with Command+Up/Down Arrows. After completing this, you can add a keystroke to the action to have quick access to it whilst editing. Adding Intro Music and Volume Automation If you wish to add some music to the intro of your podcast once you have finished the editing of the vocal tracks, just paste the file in on its own track with ripple turned on for all tracks. This will insert the music and keep all the other tracks in sync. If the music is to come in under the vocal tracks, have ripple turned off, or check out the podcast to see a quick method of moving everything around. Command+Option+V will show the volume envelope for the selected track, and then Option+L to arm that envelope. You can then add points with Shift+E and adjust the values with Numpad 2 and 8, or Command+Shift+E to add and edit a point. Fading an item out from the edit cursor is another quick way of dealing with your music. Final Levelling and Rendering If you're wanting to have chapter markers in the podcast, insert standard named project markers with the syntax CHAP=Name of chapter. Use Shift+M to add and name the markers. When it comes to rendering, in the dialog tick the Metadata checkbox, and the MP3 will have chapters included with the supplied names. There are a number of different tools to check levels and loudness within Reaper. One good option is the action Calculate loudness of master mix within time selection via dry run render. This will give an accurate representation of the final integrated loudness figure, along with the peak value, and other loudness stats. We also have the Peak Watcher, on Option+W, this can provide integrated loudness levels along with true peak throughout the editing process, however it's not covered on this podcast. To adjust your levels, again you have a lot of options at different parts of the signal chain. One is to select all the items on the track with Command+Option+A and then adjust with Command+Arrows. Or you can adjust the actual track volume with Option+Arrows, or Option+Shift+Arrows. If you have folders for the vocal tracks, this can be a good place to turn up or down the overall volume before it gets to the master track where you have a limiter. Press Command+Option+R to render out your final file with your desired settings. Typically for podcasts, for maximum compatibility, MP3 is still recommended, even though we now have better lossy formats. An integrated LUFS value near –16 dB is a generally accepted standard. List of Actions Used Option+Return - Open Project Settings Command+A - Select all items/tracks/envelope points (depending on focus) Shift+U - SWS/BR: Normalize loudness of selected items/tracks… Shift+Home - Custom: Select from cursor to start of project Control+Accent - Item: Auto trim/split items (remove silence) Command+Shift+P - OSARA: Report ripple editing mode Option+Shift+P - Options: Cycle ripple editing mode Control+Shift+Comma - Item edit: Trim left edge of item to edit cursor Control+Shift+Period - Item edit: Trim right edge of item to edit cursor Shift+A - Xenakios/SWS: Select items under edit cursor on selected tracks Command+Option+A - Item: Select all items in track Command+Up/Down - Xenakios/SWS: Nudge active take volume up/down Option+Up/Down - Track: Nudge track volume up/down Option+Shift+Up/Down - Xenakios/SWS: Nudge volume of selected tracks up/down Left Parenthesis - Time selection: Set start point Right Parenthesis - Time selection: Set end point Option+Space - Transport: Play (skip time selection) Option+Left/Right Parenthesis - Time selection: Nudge left edge left or right Command+Left/Right Parenthesis - Time selection: Nudge right edge left or right Slash - Scrub: Toggle looped-segment scrub at edit cursor Command+L - Editing.RPP Command+Option+V - OSARA: Toggle track/take volume envelope visibility (depending on focus) Option+L - OSARA: Select next track/take envelope (depending on focus) Shift+E - Envelope: Insert new point at current position (remove nearby points) Option+Shift+E - Envelope: Add/edit envelope point value at cursor Numpad 2 - Item edit: Move items/envelope points down one track/a bit Numpad 8 -  

We're on Patreon now! Find us at https://www.patreon.com/AudioUnleashed This week, Brent and Dennis kick things off with everyone's favorite: a vocabulary lesson! What's the difference between decibels and SPLs, and how are those distinct from loudness? Also, what the heck are LUFS? Next: If EarFun's new earphones are all that, why pay more? And can Sonos un-punch its own face? Buy-now links for products mentioned herein (As Amazon Associates, we may earn a small cut from qualifying purchases):

Don't sweat the noise too much! Harold talked about derressering masters, iZotope RX ninja tips, phase aligning mixes, how to use mid side eq, mastering Moogs, bass wins in big vs small control rooms, LUFS targets, and why linear eq + hip hop = bad. Get access to FREE mixing mini-course: https://MixMasterBundle.com My guest today is Harold LaRue, a GRAMMY-winning mastering engineer in Houston, Texas. Harold's recent clients include contemporary jazz masters: The Wayne Shorter Quartet, Terri Lynn Carrington, and Joey Alexander — synth sorceress: Lisa Bella Donna, and R&B legend: Jeffrey Osborne. Harold has worked as a software developer, pro-audio service manager, theatrical sound engineer, and restaurateur and was the monitor engineer for the Houston Symphony Orchestra before leaving to focus on his growing mastering business. As an audio educator, Harold has developed and taught studio production and mastering courses at universities and recording schools across the country. He continues to present public workshops and teach privately. He is a Voting Member of the Recording Academy's Producers & Engineers Wing, Audio Engineering Society, and the International Alliance of Theatrical Stage Employees. Check out our previous interview on episode RSR225 to learn more about Harold's backstory. I want to dedicate this episode to the late great audio designer and engineer Glenn Coleman whom Harold introduced me to at NAMM 2020.  THANKS TO OUR SPONSORS! http://UltimateMixingMasterclass.com https://www.adam-audio.com https://www.native-instruments.com Use code ROCK10 to get 10% off! https://www.izotope.com Use code ROCK10 to get 10% off! https://gracedesign.com/ https://RecordingStudioRockstars.com/Academy  https://www.thetoyboxstudio.com/ Listen to this guest's discography on Spotify: https://open.spotify.com/playlist/58TfiVHWwU5CSfNoamAErb?si=55dc63a967b24511 If you love the podcast, then please leave a review: https://RSRockstars.com/Review CLICK HERE FOR COMPLETE SHOW NOTES AT: https://RSRockstars.com/463

Thank you for being a subscriber to this exclusive content! ⁠SUBSCRIBE TO YOUTUBE⁠ ⁠Join the ‘Mixing Music Podcast' Discord!⁠ ⁠HIRE DEE KEI⁠ ⁠HIRE JAMES⁠ Find Dee Kei Braeden, and Jame on Social Media: Instagram: ⁠@DeeKeiMixes⁠  ⁠@JamesDeanMixes⁠ Twitter: ⁠@DeeKeiMixes⁠  ⁠CHECK OUT OUR OTHER RESOURCES⁠ Join the ‘Mixing Music Podcast' Group: ⁠Discord⁠ & ⁠Facebook⁠ The Mixing Music Podcast is sponsored by ⁠Izotope⁠, ⁠Antares (Auto Tune)⁠, ⁠Plugin Boutique⁠, ⁠Lauten Audio⁠, ⁠Spreaker⁠, ⁠Filepass⁠, & ⁠Canva⁠ The Mixing Music Podcast is a video and audio series on the art of music production and post-production. Dee Kei and Lu are both professionals in the Los Angeles music industry having worked with names like Keyshia Cole, Trey Songz, Ray J, Smokepurrp, Benny the Butcher, Sueco the Child, Ari Lennox, G-Eazy, Phresher, Lucky Daye, DDG, Lil Xan, Masego, $SNOT, Kanye West, King Kanja, Dreamville, BET, Universal Music, Interscope Records, etc. This video podcast is meant to be used for educational purposes only. This show is filmed at IN THE MIX STUDIOS located in North Hollywood, California. If you would like to sponsor the show, please email us at ⁠deekeimixes@gmail.com⁠.

You can't go a day without someone telling you how loud your music should be, these days. "Everything has to be -8 LUFS" "EDM has to be -6 LUFS or louder" "-11 is the perfect balance" "Just master everything to -14" ...but they can't all be right ! In fact, none of them are, and even more importantly, they may not even be talking about the same thing. And the differences are really important. So in this episode I try explain really clearly and concisely: The 3 different types of LUFS The difference between them Why it's crucial to know which type you're talking about Why mastering to integrated targets makes no sense When -14 LUFS isn't loud enough – and why ? Why it's not about ‘how loud', it's about how you make it loud Why integrated LUFS values are the result, not the goal of mastering Full show notes on our website https://themasteringshow.com/episode-92

Thank you for being a subscriber to this exclusive content! ⁠SUBSCRIBE TO YOUTUBE⁠ ⁠Join the ‘Mixing Music Podcast' Discord!⁠ ⁠HIRE DEE KEI⁠ ⁠HIRE JAMES⁠ Find Dee Kei Braeden, and Jame on Social Media: Instagram: ⁠@DeeKeiMixes⁠  ⁠@JamesDeanMixes⁠ Twitter: ⁠@DeeKeiMixes⁠  ⁠CHECK OUT OUR OTHER RESOURCES⁠ Join the ‘Mixing Music Podcast' Group: ⁠Discord⁠ & ⁠Facebook⁠ The Mixing Music Podcast is sponsored by ⁠Izotope⁠, ⁠Antares (Auto Tune)⁠, ⁠Plugin Boutique⁠, ⁠Lauten Audio⁠, ⁠Spreaker⁠, ⁠Filepass⁠, & ⁠Canva⁠ The Mixing Music Podcast is a video and audio series on the art of music production and post-production. Dee Kei and Lu are both professionals in the Los Angeles music industry having worked with names like Keyshia Cole, Trey Songz, Ray J, Smokepurrp, Benny the Butcher, Sueco the Child, Ari Lennox, G-Eazy, Phresher, Lucky Daye, DDG, Lil Xan, Masego, $SNOT, Kanye West, King Kanja, Dreamville, BET, Universal Music, Interscope Records, etc. This video podcast is meant to be used for educational purposes only. This show is filmed at IN THE MIX STUDIOS located in North Hollywood, California. If you would like to sponsor the show, please email us at ⁠deekeimixes@gmail.com⁠.