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The Human Voice has been widely characterized, correct?

Started by Ramon F Herrera January 11, 2015
On Wed, 14 Jan 2015 13:34:45 GMT, N0Spam@daqarta.com (Bob Masta)
wrote:

<snip>
> >The problem is that recognizing the presence or absence of a >human voice (or even recognizing the words!) doesn't help >with removing noise. It's not like you can say "aha, human >voice... now I'll just subtract out everything that *isn't* >a human voice." > >Best regards, > > >Bob Masta >
<snip> I think if our language was more like dolphins or whales we'd have a much easier time coming up with a decomposition which would stick to our "voice" and not to the noise ;-) Mark
On Wed, 14 Jan 2015 15:46:30 -0600, Ramon F Herrera
<ramon@patriot.net> wrote:

>On 1/14/2015 7:34 AM, Bob Masta wrote: >> The problem is that recognizing the presence or absence of a >> human voice (or even recognizing the words!) doesn't help >> with removing noise. It's not like you can say "aha, human >> voice... now I'll just subtract out everything that *isn't* >> a human voice." >> >> Best regards, >> >> >> Bob Masta >> > > >I respectfully beg to differ, Bob. Watch this Course: > >==================================================================== > >http://www.macprovideo.com/tutorial/izotope-rx4-audio-repair-toolbox-2 >(Free registration) > >Chapter 39. Deconstructing Tone & Noise > >The instructor claims: "It is as simple as it is amazing". > >Watch the introduction as well: > >http://play.macprovideo.com/izotope-rx4-audio-repair-toolbox-2/1 > >Jeff chose the analogy of salt getting into the food that he is cooking. >That problem can *theoretically* be solved! If we can take one molecule >at a time, we can leave the NaCl molecules out, and the rest of the >delicious the gumbo in. > >==================================================================== > >In this particular musical case the problem is not too hard: a pure-note >instrument (piano) PLUS (it is indeed an arithmetic addition) some low >frequency hum. > >Still: Sinusoids are added and subtracted.
An adaptive algorithm can be trained to remove persistent sinusoids, but that doesn't work with noise, or in fact with most real-world signals. The reason is that the frequencies and amplitudes are constantly changing... that's what constitutes "noise", and also what constitutes "speech". There isn't some specific waveform to be subtracted out, or frequency to be filtered out.
>I am sure that in the not too distant future we will be able to take the >provided Dealey Plaza audio, inform the system: > > - There is a helicopter, which sounds like this [...] > - There is a screeching train which sounds like this [...] > - There is a crying baby who sounds like this [...] > - Finally, there is the voice in which I am interested. [...]
>My initial mistake was assuming that all "noise" was garbage that could >be thrown out. That was a HUGE mistake! Any "atom" of helicopter rumble >helps to identify and subtract all its sister helicopter sounds.
>Every unit of sound there was placed by some source (IOW: God did not >add some random noise, just to mess with us). All the system has to do >is pick up every "atom" -one at a time- and figure out (yes, it is not >quite easy) in which "plate" it belongs. The number of "plates" is >finite and small. At the end, the user will have a multi-channel file, >nicely separated. In my case, I would throw away the n-1 channels, >leaving the tourist guide fellow's voice.
To use your example of atoms, the problem is that all matter is made of the same small set of atoms... it's their arrangement that makes all the difference. In the case of sound, it's an arrangement of frequencies and amplitudes in time. Those same frequencies and amplitudes could be part of a helicopter noise, train screech, or baby cry... or the speech you are interested in. Take the "shoop, shoop" of that helicopter. The individual "shoops" are bursts of noise with some particular spectrum (continuously changing, as the helicopter moves, but we'll ignore that for the moment). Suppose you come up with an algorithm that detects the "shoop"... this is not really all that difficult. Now what do you do about that? A "shoop" isn't a particular waveform that you can subtract, it's *noise*. It's made up of a broad range of frequencies, and if you filter them out you can't avoid taking significant portions of the speech with it. To use the salt-molecule-from-food example, there is no "okra molecule" or "chicken molecule"... each of those is a complex arrangement of various fats, proteins, and carbohydrates. So it may be a *lot* harder to remove the okra from the gumbo than the salt, if you are trying to work on a molecular basis. Best regards, Bob Masta DAQARTA v7.60 Data AcQuisition And Real-Time Analysis www.daqarta.com Scope, Spectrum, Spectrogram, Sound Level Meter Frequency Counter, Pitch Track, Pitch-to-MIDI FREE Signal Generator, DaqMusiq generator Science with your sound card!
On 1/14/2015 6:47 PM, Mac Decman wrote:
> I think if our language was more like dolphins or whales we'd have a > much easier time coming up with a decomposition which would stick to > our "voice" and not to the noise ;-) > > Mark >
Are you sure? I do not recall what Whalese sounds like, but let's use Dolphinese. If you fast forward to minute 4:10 you will hear a bunch of dolphins. https://www.youtube.com/watch?v=a-EAqNbpcYE What they were doing behind the stockade fence in the grassy knoll is anybody's guess. Let's just hope that an enterprising book author is not reading this NG, or this may become the latest theory in a best seller: "The Dolphins Did It!!! - THE Definite Solution to the Biggest Murder Mystery of the 20th. Century" ;-) -Ramon
On 1/15/2015 8:20 AM, Bob Masta wrote:
> An adaptive algorithm can be trained to remove persistent > sinusoids, but that doesn't work with noise, or in fact with > most real-world signals.
Bob: I barely recall my "Signals & Systems" class, which I have seldom needed since graduation a couple decades ago, but I believe somebody wrote a theorem (Fourier? Laplace?) in which he proved: "EVERY possible signal is just an addition of (possibly infinite, in the case of a step or square angle) sinusoids" If that is the case, let's say that we beg God to provide the noise sample -and She graciously obliges- which we feed into our sound remover module. ...then all noises can be removed. [I ask all readers:] Right? -Ramon
On 1/14/2015 1:14 PM, gyansorova@gmail.com wrote:
> There are Guassian Mixture Models (GMM) based approaches to remove noise of various types from speech. They work by using a code-book of speech and noise characterisation. When they find the nearest match they apply a Wiener filter frame by frame - variations of > > > http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4518754&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D4518754 >
Thanks so much for your invaluable help, gyansorova. You just confirmed my finding, and would like to report a mix of good and bad news. ======================================================= [Background Info for new readers:] For those following this thread, I am in the search/development of an *optimal* (am willing to compromise!), automated sound remover. In order to make the implementation possible, let's reduce the expectations as much as possible: assume that I am a very patient person who is willing to wait a month+, until the process is finished. Assume further that I am only interested in human voice, and there is a small set of noise sources. The signal will be decomposed into a small set of "buckets" (actually, channels used by the audio professionals), and I will pick the ones that interest me (the voice, for starters). This is the example that I am using: https://www.youtube.com/watch?v=a-EAqNbpcYE [After minute 4:30 it becomes more interesting] ======================================================= As a first step, I directed my attention to the "Audacity" initiative (because it is open source) but soon I realized that I was shooting in the wrong direction. Next, I went for help from the Sphinx folks. (1) Good News: I was right!! My guess was on the spot!! [Happy dance here] The Voice Recognition may be used for the purposes of Noise Removal!! See their reply: https://sourceforge.net/p/cmusphinx/discussion/speech-recognition/thread/1177f599/ (2) Bad News "The algorithms for audio improvement are certainly related to CMU Sphinx and could help it to improve the accuracy but they are not our primary focus right now, so it is not easy to expect us to implement noise reduction" Question: gyansorova tells us about (a) "GMM based Bayesian approach to speech enhancement in signal / transform domain" while Nickolay -the Sphinx developer and spokesperson- mentioned (b) "non-negative matrix factorization which allows you to separate speech from overlapping sounds using the vocabulary of speech atoms" Are those two methods related? The same? Completely different? TIA, -Ramon
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On 1/14/2015 6:47 PM, Mac Decman wrote:
> I think if our language was more like dolphins or whales we'd have a > much easier time coming up with a decomposition which would stick to > our "voice" and not to the noise ;-) > > Mark >
Mark: Since you are a fellow poster who clearly enjoys of humor-plus-content, there you go... http://forum.audacityteam.org/viewtopic.php?f=21&t=83110 -Ramon --------------010008020505030005060005 Content-Type: image/jpeg; name="Widening-or-Narrowing-Gap.jpg" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="Widening-or-Narrowing-Gap.jpg" /9j/4AAQSkZJRgABAAEASABIAAD//gAfTEVBRCBUZWNobm9sb2dpZXMgSW5jLiBWMS4wMQD/ 2wCEAAgFBgcGBQgHBgcJCAgJDBQNDAsLDBgREg4UHRkeHhwZHBsgJC4nICIrIhscKDYoKy8x MzQzHyY4PDgyPC4yMzEBCAkJDAoMFw0NFzEhHCExMTExMTExMTExMTExMTExMTExMTExMTEx MTExMTExMTExMTExMTExMTExMTExMTExMf/EAaIAAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYH CAkKCwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoLEAACAQMDAgQDBQUEBAAAAX0BAgMA BBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpD REVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaan qKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+foRAAIB 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On 1/15/2015 12:34 PM, Tim Wescott wrote:
> Wait! Since when has this group allowed binary attachments??? >
I was surprised myself, and trust me, I have been around for a while. -Ramon The Internet and Usenet Pioneer and Co-Founder while at MIT
On Thu, 15 Jan 2015 12:15:26 -0600, Ramon F Herrera wrote:

> On 1/14/2015 6:47 PM, Mac Decman wrote: >> I think if our language was more like dolphins or whales we'd have a >> much easier time coming up with a decomposition which would stick to >> our "voice" and not to the noise ;-) >> >> Mark >> >> > > Mark: > > Since you are a fellow poster who clearly enjoys of humor-plus-content, > there you go... > > http://forum.audacityteam.org/viewtopic.php?f=21&t=83110 > > -Ramon
Wait! Since when has this group allowed binary attachments??? -- Tim Wescott Wescott Design Services http://www.wescottdesign.com
On 1/15/2015 12:34 PM, Tim Wescott wrote:
> Wait! Since when has this group allowed binary attachments??? >
Hi, Tim: In these days of dirt-cheap hard disks and fiberoptic Internet, folks are posting video attachments. See Instagram and Disqus: http://www.politico.com/ http://instagram.com/ This reminds of the ensuing raucous reaction when I implemented the first audio server that ran at MIT. It was pretty much an on-demand rock-ola. Since the contents was only Venezuelan (plus some Latin American, tangos, etc.) music, I just knew that the owners of the copyrights would either (a) Be thrilled or (b) Blissfully ignorant for my blatant, shameless violation of their rights. I won a juicy steak on a bet that we would never be either sued (from South of the Rio Grande) or reprimanded (from North of the Charles River). Ah.... Good times... -Ramon
On Friday, January 16, 2015 at 3:18:11 AM UTC+13, Bob Masta wrote:
> On Wed, 14 Jan 2015 15:46:30 -0600, Ramon F Herrera > <ramon@patriot.net> wrote: > > >On 1/14/2015 7:34 AM, Bob Masta wrote: > >> The problem is that recognizing the presence or absence of a > >> human voice (or even recognizing the words!) doesn't help > >> with removing noise. It's not like you can say "aha, human > >> voice... now I'll just subtract out everything that *isn't* > >> a human voice." > >> > >> Best regards, > >> > >> > >> Bob Masta > >> > > > > > >I respectfully beg to differ, Bob. Watch this Course: > > > >==================================================================== > > > >http://www.macprovideo.com/tutorial/izotope-rx4-audio-repair-toolbox-2 > >(Free registration) > > > >Chapter 39. Deconstructing Tone & Noise > > > >The instructor claims: "It is as simple as it is amazing". > > > >Watch the introduction as well: > > > >http://play.macprovideo.com/izotope-rx4-audio-repair-toolbox-2/1 > > > >Jeff chose the analogy of salt getting into the food that he is cooking. > >That problem can *theoretically* be solved! If we can take one molecule > >at a time, we can leave the NaCl molecules out, and the rest of the > >delicious the gumbo in. > > > >==================================================================== > > > >In this particular musical case the problem is not too hard: a pure-note > >instrument (piano) PLUS (it is indeed an arithmetic addition) some low > >frequency hum. > > > >Still: Sinusoids are added and subtracted. > > An adaptive algorithm can be trained to remove persistent > sinusoids, but that doesn't work with noise, or in fact with > most real-world signals. The reason is that the frequencies > and amplitudes are constantly changing... that's what > constitutes "noise", and also what constitutes "speech". > There isn't some specific waveform to be subtracted out, or > frequency to be filtered out. > > >I am sure that in the not too distant future we will be able to take the > >provided Dealey Plaza audio, inform the system: > > > > - There is a helicopter, which sounds like this [...] > > - There is a screeching train which sounds like this [...] > > - There is a crying baby who sounds like this [...] > > - Finally, there is the voice in which I am interested. [...] > > >My initial mistake was assuming that all "noise" was garbage that could > >be thrown out. That was a HUGE mistake! Any "atom" of helicopter rumble > >helps to identify and subtract all its sister helicopter sounds. > > >Every unit of sound there was placed by some source (IOW: God did not > >add some random noise, just to mess with us). All the system has to do > >is pick up every "atom" -one at a time- and figure out (yes, it is not > >quite easy) in which "plate" it belongs. The number of "plates" is > >finite and small. At the end, the user will have a multi-channel file, > >nicely separated. In my case, I would throw away the n-1 channels, > >leaving the tourist guide fellow's voice. > > To use your example of atoms, the problem is that all matter > is made of the same small set of atoms... it's their > arrangement that makes all the difference. In the case of > sound, it's an arrangement of frequencies and amplitudes in > time. Those same frequencies and amplitudes could be part > of a helicopter noise, train screech, or baby cry... or the > speech you are interested in. > > Take the "shoop, shoop" of that helicopter. The individual > "shoops" are bursts of noise with some particular spectrum > (continuously changing, as the helicopter moves, but we'll > ignore that for the moment). Suppose you come up with an > algorithm that detects the "shoop"... this is not really all > that difficult. Now what do you do about that? A "shoop" > isn't a particular waveform that you can subtract, it's > *noise*. It's made up of a broad range of frequencies, and > if you filter them out you can't avoid taking significant > portions of the speech with it. > > To use the salt-molecule-from-food example, there is no > "okra molecule" or "chicken molecule"... each of those is a > complex arrangement of various fats, proteins, and > carbohydrates. So it may be a *lot* harder to remove the > okra from the gumbo than the salt, if you are trying to work > on a molecular basis. > > Best regards, > > > Bob Masta >
For a single channel speech plus noise I agree. For multiple microphone recordings of course this is not the case as seen by Blind Source Separation algorithms using Independent Component Analysis.