This thread has been closed, archived, finito, ended.

There is a follow-up, named "Part Deux"

-Ramon

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Mark:

I tracked down the research mentioned above by Emiya, Jafari, Elad, 
Gribonval and Plumbley, honestly, was less than impressed.  They have a 
little implementation in Matlab. Listen to some of their sample sounds 
(attached).

Crossed it off as a dead end.

=========================================

Now, if you want to have your socks blown off, check this out:

Theory:
http://web.media.mit.edu/~paris/Paris_Smaragdis/Paris_Smaragdis_web_page_files/mysore_lvaica2010.pdf

Practice:
http://web.engr.illinois.edu/~paris/demos/ai/user-guide.mp4

Da Man:
http://web.engr.illinois.edu/~paris/demos/

-Ramon


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On Sat, 17 Jan 2015 21:10:19 -0600, Ramon F Herrera
<ramon@conexus.net> wrote:

>On 1/16/2015 12:58 PM, gyansorova@gmail.com wrote:
>> On Saturday, January 17, 2015 at 2:53:56 AM UTC+13, Bob Masta wrote:
>>> On Thu, 15 Jan 2015 17:22:01 -0600, Ramon F Herrera
>>> <ramon@patriot.net> wrote:
>>>
>>>> On 1/15/2015 4:49 PM, Ramon F Herrera wrote:
>>>>> My guess is that iZotope RX-5 or RX-6 or RX-7 will have a full
>>>>> decomposer module. One in which if a sound file was mixed (it is simply
>>>>> arithmetic addition, Bob!) and the components were lost, they will be
>>>>> recovered.
>>>>>
>>>>> Within practical limits of course. That is why I am using helicopters,
>>>>> screeching trains and crying babies.
>>>>>
>>>>
>>>> Allow me to add this. If we asked God for the Noise Profile, She would
>>>> *cheat* and give us the negative of the full noise for the *entirety* of
>>>> the signal. That takes care of the phase mentioned by gyansorova.
>>>>
>>>> It is the same thing as the guy who plays the trumpet, sues the record
>>>> producer and his contribution (channel) gets perfectly erased from the
>>>> orchestra. A new (hopefully less litigious!) musician is hired, he does
>>>> his gig and the new channel is added.
>>>>
>>>> In real life -as Bob properly stated- we do not have the luxury of the
>>>> full noise (trumpet) signal: only a sample thereof.
>>>
>>> What Gyansorova was explaining with this:
>>> " It has to be matched in phase and amplitude which is hard
>>> for a random waveform."
>>> is that a noise profile isn't going to save the day.  If you
>>> are going to *subtract* a waveform (as opposed to filtering
>>> a frequency) you need a complete sample-by-sample version of
>>> that waveform.  With a noise profile you only know that (for
>>> example) the average noise at 1 kHz is (say) 6 dB above that
>>> at 2 kHz, and so on.  But on any given time sample the
>>> instantaneous value could be positive or negative over a
>>> huge range of amplitudes, so you have no idea what to
>>> subtract.
>>>
>>> That's quite different from a trumpet signal, where you can
>>> be assured that you have more-or-less continuous tones, so
>>> the amplitude and phase are tightly constrained from one
>>> sample to the next.
>>>
>>> One way to think of this is to look at scope image of the
>>> waveform in question. Cover up the right half and see if you
>>> can guess what it might look like, based on the left half.
>>> With a sine wave, it's trivial... just keep on with the same
>>> frequency and amplitude, starting from the last known point
>>> on the cycle.  With a trumpet it's a bit harder, but not by
>>> much.  With noise, it's essentially impossible... the values
>>> are random, so there is no correlation with prior samples
>>> (other than that related to the very loose constraint of the
>>> noise profile).
>>>
>>> However, I think your basic idea of voice recognition to
>>> remove noise could be made to work, by going the next step.
>>> Instead of just recognizing the presence of speech, the
>>> system could build a model of the vocal tract of the
>>> speaker, given enough speech to work on.  Then it could go
>>> back and compare its output to the raw waveform.  Only the
>>> model sound would be used in the final output, but guided by
>>> the raw waveform.  The model would be like a flywheel that
>>> can coast through areas that don't match its capabilities,
>>> such as a helicopter "swoop" in the midst of a sustained
>>> vowel.
>>>
>>> Oughta be good for a doctoral thesis or two...
>>>
>>> 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!
>>
>> Like this maybe?
>>
>> http://www.eurasip.org/Proceedings/Eusipco/Eusipco2008/papers/1569105040.pdf
>
>
>... or perhaps this?
>
>   "This approach is shown to outperform state-of-the-art and 
>commercially available methods for audio declipping in terms of 
>Signal-to-Noise Ratio."
>
>    http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6020748
>
>-Ramon

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6570655

On 1/16/2015 12:58 PM, gyansorova@gmail.com wrote:
> On Saturday, January 17, 2015 at 2:53:56 AM UTC+13, Bob Masta wrote:
>> On Thu, 15 Jan 2015 17:22:01 -0600, Ramon F Herrera
>> <ramon@patriot.net> wrote:
>>
>>> On 1/15/2015 4:49 PM, Ramon F Herrera wrote:
>>>> My guess is that iZotope RX-5 or RX-6 or RX-7 will have a full
>>>> decomposer module. One in which if a sound file was mixed (it is simply
>>>> arithmetic addition, Bob!) and the components were lost, they will be
>>>> recovered.
>>>>
>>>> Within practical limits of course. That is why I am using helicopters,
>>>> screeching trains and crying babies.
>>>>
>>>
>>> Allow me to add this. If we asked God for the Noise Profile, She would
>>> *cheat* and give us the negative of the full noise for the *entirety* of
>>> the signal. That takes care of the phase mentioned by gyansorova.
>>>
>>> It is the same thing as the guy who plays the trumpet, sues the record
>>> producer and his contribution (channel) gets perfectly erased from the
>>> orchestra. A new (hopefully less litigious!) musician is hired, he does
>>> his gig and the new channel is added.
>>>
>>> In real life -as Bob properly stated- we do not have the luxury of the
>>> full noise (trumpet) signal: only a sample thereof.
>>
>> What Gyansorova was explaining with this:
>> " It has to be matched in phase and amplitude which is hard
>> for a random waveform."
>> is that a noise profile isn't going to save the day.  If you
>> are going to *subtract* a waveform (as opposed to filtering
>> a frequency) you need a complete sample-by-sample version of
>> that waveform.  With a noise profile you only know that (for
>> example) the average noise at 1 kHz is (say) 6 dB above that
>> at 2 kHz, and so on.  But on any given time sample the
>> instantaneous value could be positive or negative over a
>> huge range of amplitudes, so you have no idea what to
>> subtract.
>>
>> That's quite different from a trumpet signal, where you can
>> be assured that you have more-or-less continuous tones, so
>> the amplitude and phase are tightly constrained from one
>> sample to the next.
>>
>> One way to think of this is to look at scope image of the
>> waveform in question. Cover up the right half and see if you
>> can guess what it might look like, based on the left half.
>> With a sine wave, it's trivial... just keep on with the same
>> frequency and amplitude, starting from the last known point
>> on the cycle.  With a trumpet it's a bit harder, but not by
>> much.  With noise, it's essentially impossible... the values
>> are random, so there is no correlation with prior samples
>> (other than that related to the very loose constraint of the
>> noise profile).
>>
>> However, I think your basic idea of voice recognition to
>> remove noise could be made to work, by going the next step.
>> Instead of just recognizing the presence of speech, the
>> system could build a model of the vocal tract of the
>> speaker, given enough speech to work on.  Then it could go
>> back and compare its output to the raw waveform.  Only the
>> model sound would be used in the final output, but guided by
>> the raw waveform.  The model would be like a flywheel that
>> can coast through areas that don't match its capabilities,
>> such as a helicopter "swoop" in the midst of a sustained
>> vowel.
>>
>> Oughta be good for a doctoral thesis or two...
>>
>> 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!
>
> Like this maybe?
>
> http://www.eurasip.org/Proceedings/Eusipco/Eusipco2008/papers/1569105040.pdf


... or perhaps this?

   "This approach is shown to outperform state-of-the-art and 
commercially available methods for audio declipping in terms of 
Signal-to-Noise Ratio."

    http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6020748

-Ramon

On Saturday, January 17, 2015 at 2:53:56 AM UTC+13, Bob Masta wrote:
> On Thu, 15 Jan 2015 17:22:01 -0600, Ramon F Herrera
> <ramon@patriot.net> wrote:
> 
> >On 1/15/2015 4:49 PM, Ramon F Herrera wrote:
> >> My guess is that iZotope RX-5 or RX-6 or RX-7 will have a full
> >> decomposer module. One in which if a sound file was mixed (it is simply
> >> arithmetic addition, Bob!) and the components were lost, they will be
> >> recovered.
> >>
> >> Within practical limits of course. That is why I am using helicopters,
> >> screeching trains and crying babies.
> >>
> >
> >Allow me to add this. If we asked God for the Noise Profile, She would 
> >*cheat* and give us the negative of the full noise for the *entirety* of 
> >the signal. That takes care of the phase mentioned by gyansorova.
> >
> >It is the same thing as the guy who plays the trumpet, sues the record 
> >producer and his contribution (channel) gets perfectly erased from the 
> >orchestra. A new (hopefully less litigious!) musician is hired, he does 
> >his gig and the new channel is added.
> >
> >In real life -as Bob properly stated- we do not have the luxury of the 
> >full noise (trumpet) signal: only a sample thereof.
> 
> What Gyansorova was explaining with this:
> " It has to be matched in phase and amplitude which is hard
> for a random waveform."
> is that a noise profile isn't going to save the day.  If you
> are going to *subtract* a waveform (as opposed to filtering
> a frequency) you need a complete sample-by-sample version of
> that waveform.  With a noise profile you only know that (for
> example) the average noise at 1 kHz is (say) 6 dB above that
> at 2 kHz, and so on.  But on any given time sample the
> instantaneous value could be positive or negative over a
> huge range of amplitudes, so you have no idea what to
> subtract. 
> 
> That's quite different from a trumpet signal, where you can
> be assured that you have more-or-less continuous tones, so
> the amplitude and phase are tightly constrained from one
> sample to the next.
> 
> One way to think of this is to look at scope image of the
> waveform in question. Cover up the right half and see if you
> can guess what it might look like, based on the left half.
> With a sine wave, it's trivial... just keep on with the same
> frequency and amplitude, starting from the last known point
> on the cycle.  With a trumpet it's a bit harder, but not by
> much.  With noise, it's essentially impossible... the values
> are random, so there is no correlation with prior samples
> (other than that related to the very loose constraint of the
> noise profile).
> 
> However, I think your basic idea of voice recognition to
> remove noise could be made to work, by going the next step.
> Instead of just recognizing the presence of speech, the
> system could build a model of the vocal tract of the
> speaker, given enough speech to work on.  Then it could go
> back and compare its output to the raw waveform.  Only the
> model sound would be used in the final output, but guided by
> the raw waveform.  The model would be like a flywheel that
> can coast through areas that don't match its capabilities,
> such as a helicopter "swoop" in the midst of a sustained
> vowel.
> 
> Oughta be good for a doctoral thesis or two...
> 
> 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!

Like this maybe?

http://www.eurasip.org/Proceedings/Eusipco/Eusipco2008/papers/1569105040.pdf

On Thu, 15 Jan 2015 17:22:01 -0600, Ramon F Herrera
<ramon@patriot.net> wrote:

>On 1/15/2015 4:49 PM, Ramon F Herrera wrote:
>> My guess is that iZotope RX-5 or RX-6 or RX-7 will have a full
>> decomposer module. One in which if a sound file was mixed (it is simply
>> arithmetic addition, Bob!) and the components were lost, they will be
>> recovered.
>>
>> Within practical limits of course. That is why I am using helicopters,
>> screeching trains and crying babies.
>>
>
>Allow me to add this. If we asked God for the Noise Profile, She would 
>*cheat* and give us the negative of the full noise for the *entirety* of 
>the signal. That takes care of the phase mentioned by gyansorova.
>
>It is the same thing as the guy who plays the trumpet, sues the record 
>producer and his contribution (channel) gets perfectly erased from the 
>orchestra. A new (hopefully less litigious!) musician is hired, he does 
>his gig and the new channel is added.
>
>In real life -as Bob properly stated- we do not have the luxury of the 
>full noise (trumpet) signal: only a sample thereof.

What Gyansorova was explaining with this:
" It has to be matched in phase and amplitude which is hard
for a random waveform."
is that a noise profile isn't going to save the day.  If you
are going to *subtract* a waveform (as opposed to filtering
a frequency) you need a complete sample-by-sample version of
that waveform.  With a noise profile you only know that (for
example) the average noise at 1 kHz is (say) 6 dB above that
at 2 kHz, and so on.  But on any given time sample the
instantaneous value could be positive or negative over a
huge range of amplitudes, so you have no idea what to
subtract. 

That's quite different from a trumpet signal, where you can
be assured that you have more-or-less continuous tones, so
the amplitude and phase are tightly constrained from one
sample to the next.

One way to think of this is to look at scope image of the
waveform in question. Cover up the right half and see if you
can guess what it might look like, based on the left half.
With a sine wave, it's trivial... just keep on with the same
frequency and amplitude, starting from the last known point
on the cycle.  With a trumpet it's a bit harder, but not by
much.  With noise, it's essentially impossible... the values
are random, so there is no correlation with prior samples
(other than that related to the very loose constraint of the
noise profile).

However, I think your basic idea of voice recognition to
remove noise could be made to work, by going the next step.
Instead of just recognizing the presence of speech, the
system could build a model of the vocal tract of the
speaker, given enough speech to work on.  Then it could go
back and compare its output to the raw waveform.  Only the
model sound would be used in the final output, but guided by
the raw waveform.  The model would be like a flywheel that
can coast through areas that don't match its capabilities,
such as a helicopter "swoop" in the midst of a sustained
vowel.

Oughta be good for a doctoral thesis or two...

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 1/15/2015 4:49 PM, Ramon F Herrera wrote:
> My guess is that iZotope RX-5 or RX-6 or RX-7 will have a full
> decomposer module. One in which if a sound file was mixed (it is simply
> arithmetic addition, Bob!) and the components were lost, they will be
> recovered.
>
> Within practical limits of course. That is why I am using helicopters,
> screeching trains and crying babies.
>

Allow me to add this. If we asked God for the Noise Profile, She would 
*cheat* and give us the negative of the full noise for the *entirety* of 
the signal. That takes care of the phase mentioned by gyansorova.

It is the same thing as the guy who plays the trumpet, sues the record 
producer and his contribution (channel) gets perfectly erased from the 
orchestra. A new (hopefully less litigious!) musician is hired, he does 
his gig and the new channel is added.

In real life -as Bob properly stated- we do not have the luxury of the 
full noise (trumpet) signal: only a sample thereof.

-Ramon

On 1/15/2015 3:41 PM, gyansorova@gmail.com wrote:
> On Friday, January 16, 2015 at 6:12:35 AM UTC+13, Ramon F Herrera wrote:
>> 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
>
> It has to be matched in phase and amplitude which is hard for a random waveform.
>

Precisely.

Attorneys have a maxim (*):

     "Give me all evidences and I will win all cases".

My contention (actually, I am not 100% sure, as skeptical, some doubt 
remains, and would love to clarify this) is:

     Give me the precise Noise Profile and I will clean all noises.

The optimal Noise Profile is the negative of the noise (i.e., the full
description of the noise), we add it arithmetically to the polluted
signal and voila! the pristine signal is the result.

It may be hard, but it is doable and with faster machines and better
algorithms we are getting there.

Furthermore, we can stipulate: "The helicopter sound/noise begins at
minute 4:37 and ends at minute 6:03. Do not bother looking for it
outside that range".

My guess is that iZotope RX-5 or RX-6 or RX-7 will have a full
decomposer module. One in which if a sound file was mixed (it is simply
arithmetic addition, Bob!) and the components were lost, they will be
recovered.

Within practical limits of course. That is why I am using helicopters,
screeching trains and crying babies.

-Ramon

(*) In Spanish speaking countries, anyway. Not sure about USA, etc.

On 1/15/2015 3:41 PM, gyansorova@gmail.com wrote:
> On Friday, January 16, 2015 at 6:12:35 AM UTC+13, Ramon F Herrera wrote:
>> 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
>
> It has to be matched in phase and amplitude which is hard for a random waveform.
>

Precisely. Attorneys have a maxim (*):

     "Give me all evidences and I will win all cases".

My contention (actually, I am not 100% sure, doubt remains, and would 
love to clarify this) is:

     Give me the precise Noise Profile and I will clean all noises.

The optimal Noise Profile is the negative of the noise (i.e., the full 
description of the noise), we add it arithmetically to the polluted 
signal and voila! the pristine signal is the result.

It may be hard, but it is doable and with faster machines and better 
algorithms we are getting there.

Furthermore, we can stipulate: "The helicopter sound/noise begins at 
minute 4:37 and ends at minute 6:03. Do not bother looking for it 
outside that range".

My guess is that iZotope RX-5 or RX-6 or RX-7 will have a full 
decomposer module. One in which if a sound file was mixed (it is simply 
arithmetic addition, Bob!) and the components were lost, they will be 
recovered.

Within practical limits of course. That is why I am using helicopters, 
screeching trains and crying babies.

-Ramon

(*) In Spanish speaking countries, anyway. Not sure about USA, etc.

On Friday, January 16, 2015 at 6:12:35 AM UTC+13, Ramon F Herrera wrote:
> 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

It has to be matched in phase and amplitude which is hard for a random waveform.