So , does it g does that mean it gets worse ? Or  ?

No. It actually improves over the baseline of not having a Wiener filter in the whole system. Like I have an LDA f LDA plus on - line normalization , and then I plug in the Wiener filter in that ,

so it improves over not having the Wiener filter. So it improves but it  it doesn't take it like be beyond like thirty percent over the baseline. So


No. It actually improves over the baseline of not having a Wiener filter in the whole system. Like I have an LDA f LDA plus on - line normalization , and then I plug in the Wiener filter in that ,

so it improves over not having the Wiener filter. So it improves but it  it doesn't take it like be beyond like thirty percent over the baseline. So

But that 's what I 'm confused about , cuz I think  I thought that our system was more like forty percent without the Wiener filtering.


so it improves over not having the Wiener filter. So it improves but it  it doesn't take it like be beyond like thirty percent over the baseline. So

But that 's what I 'm confused about , cuz I think  I thought that our system was more like forty percent without the Wiener filtering.


But that 's what I 'm confused about , cuz I think  I thought that our system was more like forty percent without the Wiener filtering.


well , these are not  No , it 's the old VAD. So my baseline was ,  uh ,  nine  This is like  w the baseline is ninety - five point six eight , and eighty - nine , and


well , these are not  No , it 's the old VAD. So my baseline was ,  uh ,  nine  This is like  w the baseline is ninety - five point six eight , and eighty - nine , and

So I mean , if you can do all these in word errors it 's a lot  a lot easier actually.


well , these are not  No , it 's the old VAD. So my baseline was ,  uh ,  nine  This is like  w the baseline is ninety - five point six eight , and eighty - nine , and

So I mean , if you can do all these in word errors it 's a lot  a lot easier actually.

If you do all these in word error rates it 's a lot easier , right ?


So I mean , if you can do all these in word errors it 's a lot  a lot easier actually.

If you do all these in word error rates it 's a lot easier , right ?

Oh , OK , OK , OK. Errors , right , I don't have.


If you do all these in word error rates it 's a lot easier , right ?

Oh , OK , OK , OK. Errors , right , I don't have.

OK , cuz then you can figure out the percentages.


Oh , OK , OK , OK. Errors , right , I don't have.

OK , cuz then you can figure out the percentages.


OK , cuz then you can figure out the percentages.

The baseline is something similar to a w I mean , the t the  the baseline that you are talking about is the MFCC baseline , right ?


The baseline is something similar to a w I mean , the t the  the baseline that you are talking about is the MFCC baseline , right ?


The baseline is something similar to a w I mean , the t the  the baseline that you are talking about is the MFCC baseline , right ?

OK. So the baseline  One baseline is MFCC baseline that  When I said thirty percent improvement it 's like MFCC baseline.


OK. So the baseline  One baseline is MFCC baseline that  When I said thirty percent improvement it 's like MFCC baseline.


OK. So the baseline  One baseline is MFCC baseline that  When I said thirty percent improvement it 's like MFCC baseline.

So  so  so what 's it start on ? The MFCC baseline is  is what ? Is at what level ?


OK. So the baseline  One baseline is MFCC baseline that  When I said thirty percent improvement it 's like MFCC baseline.

So  so  so what 's it start on ? The MFCC baseline is  is what ? Is at what level ?

It 's the  it 's just the mel frequency and that 's it.


So  so  so what 's it start on ? The MFCC baseline is  is what ? Is at what level ?

It 's the  it 's just the mel frequency and that 's it.


So  so  so what 's it start on ? The MFCC baseline is  is what ? Is at what level ?

It 's the  it 's just the mel frequency and that 's it.

Uh , so I I don't have that number here. OK , OK , OK , I have it here. Uh , it 's the VAD plus the baseline actually. I 'm talking about the  the MFCC plus I do a frame dropping on it. So that 's like  the word error rate is like four point three. Like  Ten point seven.


It 's the  it 's just the mel frequency and that 's it.

Uh , so I I don't have that number here. OK , OK , OK , I have it here. Uh , it 's the VAD plus the baseline actually. I 'm talking about the  the MFCC plus I do a frame dropping on it. So that 's like  the word error rate is like four point three. Like  Ten point seven.

Four point three. What 's ten point seven ?


Uh , so I I don't have that number here. OK , OK , OK , I have it here. Uh , it 's the VAD plus the baseline actually. I 'm talking about the  the MFCC plus I do a frame dropping on it. So that 's like  the word error rate is like four point three. Like  Ten point seven.

Four point three. What 's ten point seven ?

It 's a medium misma OK , sorry. There 's a well ma well matched , medium mismatched , and a high matched.


Four point three. What 's ten point seven ?

It 's a medium misma OK , sorry. There 's a well ma well matched , medium mismatched , and a high matched.


It 's a medium misma OK , sorry. There 's a well ma well matched , medium mismatched , and a high matched.


OK , four point three , ten point seven ,


Yeah , it 's like ten point one. Still the same. And the high mismatch is like eighteen point five.


Oh , the one is  this one is just the baseline plus the , uh , Wiener filter plugged into it.


Oh , the one is  this one is just the baseline plus the , uh , Wiener filter plugged into it.

But where 's the , uh , on - line normalization and so on ?


Oh , the one is  this one is just the baseline plus the , uh , Wiener filter plugged into it.

But where 's the , uh , on - line normalization and so on ?

Oh , OK. So  Sorry. So , with the  with the on - line normalization , the performance was , um , ten  OK , so it 's like four point three. Uh , and again , that 's the ba the ten point , uh , four and twenty point one. That was with on - line normalization and LDA. So the h well matched has like literally not changed by adding on - line or LDA on it. But the  I mean , even the medium mismatch is pretty much the same. And the high mismatch was improved by twenty percent absolute.


Oh , the one is  this one is just the baseline plus the , uh , Wiener filter plugged into it.

But where 's the , uh , on - line normalization and so on ?

Oh , OK. So  Sorry. So , with the  with the on - line normalization , the performance was , um , ten  OK , so it 's like four point three. Uh , and again , that 's the ba the ten point , uh , four and twenty point one. That was with on - line normalization and LDA. So the h well matched has like literally not changed by adding on - line or LDA on it. But the  I mean , even the medium mismatch is pretty much the same. And the high mismatch was improved by twenty percent absolute.

OK , and what kind of number  an and what are we talking about here ?


But where 's the , uh , on - line normalization and so on ?

Oh , OK. So  Sorry. So , with the  with the on - line normalization , the performance was , um , ten  OK , so it 's like four point three. Uh , and again , that 's the ba the ten point , uh , four and twenty point one. That was with on - line normalization and LDA. So the h well matched has like literally not changed by adding on - line or LDA on it. But the  I mean , even the medium mismatch is pretty much the same. And the high mismatch was improved by twenty percent absolute.

OK , and what kind of number  an and what are we talking about here ?


Oh , OK. So  Sorry. So , with the  with the on - line normalization , the performance was , um , ten  OK , so it 's like four point three. Uh , and again , that 's the ba the ten point , uh , four and twenty point one. That was with on - line normalization and LDA. So the h well matched has like literally not changed by adding on - line or LDA on it. But the  I mean , even the medium mismatch is pretty much the same. And the high mismatch was improved by twenty percent absolute.

OK , and what kind of number  an and what are we talking about here ?


OK , and what kind of number  an and what are we talking about here ?


And what did  So , what was the , um , uh , corresponding number , say , for , um , uh , the Alcatel system for instance ?


Yep , it 's three point four , uh , eight point , uh , seven , and , uh , thirteen point seven.


So , uh , this is the single stage Wiener filter , with  The noise estimation was based on first ten frames.


So , uh , this is the single stage Wiener filter , with  The noise estimation was based on first ten frames.

Actually I started with  using the VAD to estimate the noise and then I found that it works  it doesn't work for Finnish and Spanish because the VAD endpoints are not good to estimate the noise because it cuts into the speech sometimes , so I end up overestimating the noise and getting a worse result. So it works only for Italian by u for  using a VAD to estimate noise.


Actually I started with  using the VAD to estimate the noise and then I found that it works  it doesn't work for Finnish and Spanish because the VAD endpoints are not good to estimate the noise because it cuts into the speech sometimes , so I end up overestimating the noise and getting a worse result. So it works only for Italian by u for  using a VAD to estimate noise.

It works for Italian because the VAD was trained on Italian.


It works for Italian because the VAD was trained on Italian.

So , uh  so this was , uh  And so this was giving  um , this  this was like not improving a lot on this baseline of not having the Wiener filter on it. And , so , uh , I ran this stuff with one more stage of Wiener filtering on it but the second time , what I did was I  estimated the new Wiener filter based on the cleaned up speech , and did , uh , smoothing in the frequency to  to reduce the variance


So , uh  so this was , uh  And so this was giving  um , this  this was like not improving a lot on this baseline of not having the Wiener filter on it. And , so , uh , I ran this stuff with one more stage of Wiener filtering on it but the second time , what I did was I  estimated the new Wiener filter based on the cleaned up speech , and did , uh , smoothing in the frequency to  to reduce the variance

I mean , I have  I 've  I 've observed there are , like , a lot of bumps in the frequency when I do this Wiener filtering which is more like a musical noise or something. And so by adding another stage of Wiener filtering , the results on the SpeechDat - Car was like , um  So , I still don't have the word error rate. I 'm sorry about it. But the overall improvement was like fifty - six point four six. This was again using ten frames of noise estimate and two stage of Wiener filtering. And the rest is like the LDA plu and the on - line normalization all remaining the same. Uh , so this was , like , compared to , uh , uh  Fifty - seven is what you got by using the French Telecom system , right ?


I mean , I have  I 've  I 've observed there are , like , a lot of bumps in the frequency when I do this Wiener filtering which is more like a musical noise or something. And so by adding another stage of Wiener filtering , the results on the SpeechDat - Car was like , um  So , I still don't have the word error rate. I 'm sorry about it. But the overall improvement was like fifty - six point four six. This was again using ten frames of noise estimate and two stage of Wiener filtering. And the rest is like the LDA plu and the on - line normalization all remaining the same. Uh , so this was , like , compared to , uh , uh  Fifty - seven is what you got by using the French Telecom system , right ?


No , this is over the whole SpeechDat - Car. So


Yeah , so the new  the new Wiener filtering schema is like  some fifty - six point four six which is like one percent still less than what you got using the French Telecom system.


Yeah , so the new  the new Wiener filtering schema is like  some fifty - six point four six which is like one percent still less than what you got using the French Telecom system.

But it 's a pretty similar number in any event.


But it 's a pretty similar number in any event.

Yeah. But again , you 're  you 're more or less doing what they were doing , right ?


But it 's a pretty similar number in any event.

Yeah. But again , you 're  you 're more or less doing what they were doing , right ?

It 's  it 's different in a sense like I 'm actually cleaning up the cleaned up spectrum which they 're not doing. They 're d what they 're doing is , they have two stage  stages of estimating the Wiener filter , but  the final filter , what they do is they  they take it to their time domain by doing an inverse Fourier transform.


Yeah. But again , you 're  you 're more or less doing what they were doing , right ?

It 's  it 's different in a sense like I 'm actually cleaning up the cleaned up spectrum which they 're not doing. They 're d what they 're doing is , they have two stage  stages of estimating the Wiener filter , but  the final filter , what they do is they  they take it to their time domain by doing an inverse Fourier transform.


Yeah. But again , you 're  you 're more or less doing what they were doing , right ?

It 's  it 's different in a sense like I 'm actually cleaning up the cleaned up spectrum which they 're not doing. They 're d what they 're doing is , they have two stage  stages of estimating the Wiener filter , but  the final filter , what they do is they  they take it to their time domain by doing an inverse Fourier transform.

And they filter the original signal using that fil filter ,


It 's  it 's different in a sense like I 'm actually cleaning up the cleaned up spectrum which they 're not doing. They 're d what they 're doing is , they have two stage  stages of estimating the Wiener filter , but  the final filter , what they do is they  they take it to their time domain by doing an inverse Fourier transform.

And they filter the original signal using that fil filter ,


And they filter the original signal using that fil filter ,

which is like final filter is acting on the input noisy speech rather than on the cleaned up. So this is more like I 'm doing Wiener filter twice , but the only thing is that the second time I 'm actually smoothing the filter and then cleaning up the cleaned up spectrum first level. And so that  that 's  that 's what the difference is.


which is like final filter is acting on the input noisy speech rather than on the cleaned up. So this is more like I 'm doing Wiener filter twice , but the only thing is that the second time I 'm actually smoothing the filter and then cleaning up the cleaned up spectrum first level. And so that  that 's  that 's what the difference is.

And actually I tried it on s the original clean  I mean , the original spectrum where , like , I  the second time I estimate the filter but actually clean up the noisy speech rather the c s first  output of the first stage and that doesn't  seems to be a  giving , I mean , that much improvement. I  I didn didn't run it for the whole case. And  and what I t what I tried was , by using the same thing but  Uh , so we actually found that the VAD is very , like , crucial. I mean , just by changing the VAD itself gives you the  a lot of improvement


And actually I tried it on s the original clean  I mean , the original spectrum where , like , I  the second time I estimate the filter but actually clean up the noisy speech rather the c s first  output of the first stage and that doesn't  seems to be a  giving , I mean , that much improvement. I  I didn didn't run it for the whole case. And  and what I t what I tried was , by using the same thing but  Uh , so we actually found that the VAD is very , like , crucial. I mean , just by changing the VAD itself gives you the  a lot of improvement

by instead of using the current VAD , if you just take up the VAD output from the channel zero ,  when  instead of using channel zero and channel one , because that was the p that was the reason why I was not getting a lot of improvement for estimating  the noise. So I just used the channel zero VAD to estimate the noise so that it gives me some reliable mar markers for this noise estimation.


by instead of using the current VAD , if you just take up the VAD output from the channel zero ,  when  instead of using channel zero and channel one , because that was the p that was the reason why I was not getting a lot of improvement for estimating  the noise. So I just used the channel zero VAD to estimate the noise so that it gives me some reliable mar markers for this noise estimation.


So it 's the close - talking microphone.


So it 's the close - talking microphone.

So because the channel zero and channel one are like the same speech , but only w I mean , the same endpoints.


So because the channel zero and channel one are like the same speech , but only w I mean , the same endpoints.

But the only thing is that the speech is very noisy for channel one , so you can actually use the output of the channel zero for channel one for the VAD. I mean , that 's like a cheating method.


So because the channel zero and channel one are like the same speech , but only w I mean , the same endpoints.

But the only thing is that the speech is very noisy for channel one , so you can actually use the output of the channel zero for channel one for the VAD. I mean , that 's like a cheating method.

Right. I mean , so a are they going to pro What are they doing to do , do we know yet ? about  as far as what they 're  what the rules are going to be and what we can use ?


So because the channel zero and channel one are like the same speech , but only w I mean , the same endpoints.

But the only thing is that the speech is very noisy for channel one , so you can actually use the output of the channel zero for channel one for the VAD. I mean , that 's like a cheating method.

Right. I mean , so a are they going to pro What are they doing to do , do we know yet ? about  as far as what they 're  what the rules are going to be and what we can use ?

Yeah , so actually I received a  a new document , describing this.


But the only thing is that the speech is very noisy for channel one , so you can actually use the output of the channel zero for channel one for the VAD. I mean , that 's like a cheating method.

Right. I mean , so a are they going to pro What are they doing to do , do we know yet ? about  as far as what they 're  what the rules are going to be and what we can use ?

Yeah , so actually I received a  a new document , describing this.


Right. I mean , so a are they going to pro What are they doing to do , do we know yet ? about  as far as what they 're  what the rules are going to be and what we can use ?

Yeah , so actually I received a  a new document , describing this.

And what they did finally is to , mmm , uh , not to align the utterances but to perform recognition , um , only on the close - talking microphone ,


Yeah , so actually I received a  a new document , describing this.

And what they did finally is to , mmm , uh , not to align the utterances but to perform recognition , um , only on the close - talking microphone ,


And what they did finally is to , mmm , uh , not to align the utterances but to perform recognition , um , only on the close - talking microphone ,

and to take the result of the recognition to get the boundaries uh , of speech.


And what they did finally is to , mmm , uh , not to align the utterances but to perform recognition , um , only on the close - talking microphone ,

and to take the result of the recognition to get the boundaries uh , of speech.

So it 's not like that 's being done in one place or one time.


and to take the result of the recognition to get the boundaries uh , of speech.

So it 's not like that 's being done in one place or one time.


and to take the result of the recognition to get the boundaries uh , of speech.

So it 's not like that 's being done in one place or one time.

That 's  that 's just a rule and we 'd  you  you were permitted to do that. Is  is that it ?


So it 's not like that 's being done in one place or one time.

That 's  that 's just a rule and we 'd  you  you were permitted to do that. Is  is that it ?

Uh , I think they will send , um , files but we  we don't  Well , apparently


That 's  that 's just a rule and we 'd  you  you were permitted to do that. Is  is that it ?

Uh , I think they will send , um , files but we  we don't  Well , apparently

Oh , so they will send files so everybody will have the same boundaries to work with ?


Uh , I think they will send , um , files but we  we don't  Well , apparently

Oh , so they will send files so everybody will have the same boundaries to work with ?

But actually their alignment actually is not seems to be improving in like on all cases.


Oh , so they will send files so everybody will have the same boundaries to work with ?

But actually their alignment actually is not seems to be improving in like on all cases.


But actually their alignment actually is not seems to be improving in like on all cases.

Oh , i Yeah , so what happened here is that , um , the overall improvement that they have with this method  So  Well , to be more precise , what they have is , they have these alignments and then they drop the beginning silence and  and the end silence but they keep , uh , two hundred milliseconds before speech and two hundred after speech. And they keep the speech pauses also. Um , and the overall improvement over the MFCC baseline So , when they just , uh , add this frame dropping in addition it 's r uh , forty percent , right ?


Oh , i Yeah , so what happened here is that , um , the overall improvement that they have with this method  So  Well , to be more precise , what they have is , they have these alignments and then they drop the beginning silence and  and the end silence but they keep , uh , two hundred milliseconds before speech and two hundred after speech. And they keep the speech pauses also. Um , and the overall improvement over the MFCC baseline So , when they just , uh , add this frame dropping in addition it 's r uh , forty percent , right ?


Um , which is , um , t which is the overall improvement. But in some cases it doesn't improve at all. Like , uh , y do you remember which case ?


Um , which is , um , t which is the overall improvement. But in some cases it doesn't improve at all. Like , uh , y do you remember which case ?

It gives like negative  Well , in  in like some Italian and TI - digits ,


It gives like negative  Well , in  in like some Italian and TI - digits ,


Yeah. So by using the endpointed speech , actually it 's worse than the baseline in some instances , which could be due to the word pattern.


Yeah. So by using the endpointed speech , actually it 's worse than the baseline in some instances , which could be due to the word pattern.

And  Yeah , the other thing also is that fourteen percent is less than what you obtain using a real VAD.


And  Yeah , the other thing also is that fourteen percent is less than what you obtain using a real VAD.


So  Uh  So I think this shows that there is still work  Uh , well , working on the VAD is still  still important I think.


So  Uh  So I think this shows that there is still work  Uh , well , working on the VAD is still  still important I think.

Can I ask just a  a high level question ? Can you just say like one or two sentences about Wiener filtering and why  why are people doing that ?


Can I ask just a  a high level question ? Can you just say like one or two sentences about Wiener filtering and why  why are people doing that ?


Can I ask just a  a high level question ? Can you just say like one or two sentences about Wiener filtering and why  why are people doing that ?

OK , so the Wiener filter , it 's  it 's like  it 's like you try to minimize  I mean , so the basic principle of Wiener filter is like you try to minimize the , uh , d uh , difference between the noisy signal and the clean signal if you have two channels. Like let 's say you have a clean t signal and you have an additional channel where you know what is the noisy signal.


OK , so the Wiener filter , it 's  it 's like  it 's like you try to minimize  I mean , so the basic principle of Wiener filter is like you try to minimize the , uh , d uh , difference between the noisy signal and the clean signal if you have two channels. Like let 's say you have a clean t signal and you have an additional channel where you know what is the noisy signal.


OK , so the Wiener filter , it 's  it 's like  it 's like you try to minimize  I mean , so the basic principle of Wiener filter is like you try to minimize the , uh , d uh , difference between the noisy signal and the clean signal if you have two channels. Like let 's say you have a clean t signal and you have an additional channel where you know what is the noisy signal.

And then you try to minimize the error between these two.


And then you try to minimize the error between these two.

So that 's the basic principle. And you get  you can do that  I mean , if  if you have only a c noisy signal , at a level which you , you w try to estimate the noise from the w assuming that the first few frames are noise or if you have a w voice activity detector , uh , you estimate the noise spectrum.


So that 's the basic principle. And you get  you can do that  I mean , if  if you have only a c noisy signal , at a level which you , you w try to estimate the noise from the w assuming that the first few frames are noise or if you have a w voice activity detector , uh , you estimate the noise spectrum.


Yeah. in  yeah , after the speech starts.


Yeah. in  yeah , after the speech starts.

So  but that 's not the case in , uh , many  many of our cases but it works reasonably well.


So  but that 's not the case in , uh , many  many of our cases but it works reasonably well.

And  and then you What you do is you , uh b fff. So again , I can write down some of these eq Oh , OK. Yeah. And then you do this  uh , this is the transfer function of the Wiener filter , so " SF " is a clean speech spectrum , power spectrum


And  and then you What you do is you , uh b fff. So again , I can write down some of these eq Oh , OK. Yeah. And then you do this  uh , this is the transfer function of the Wiener filter , so " SF " is a clean speech spectrum , power spectrum

And " N " is the noisy power spectrum. And so this is the transfer function.


And " N " is the noisy power spectrum. And so this is the transfer function.


And then you multiply your noisy power spectrum with this. You get an estimate of the clean power spectrum.


And then you multiply your noisy power spectrum with this. You get an estimate of the clean power spectrum.

So  but the thing is that you have to estimate the SF from the noisy spectrum , what you have. So you estimate the NF from the initial noise portions and then you subtract that from the current noisy spectrum to get an estimate of the SF. So sometimes that becomes zero because you do you don't have a true estimate of the noise. So the f filter will have like sometimes zeros in it


So  but the thing is that you have to estimate the SF from the noisy spectrum , what you have. So you estimate the NF from the initial noise portions and then you subtract that from the current noisy spectrum to get an estimate of the SF. So sometimes that becomes zero because you do you don't have a true estimate of the noise. So the f filter will have like sometimes zeros in it

because some frequency values will be zeroed out because of that. And that creates a lot of discontinuities across the spectrum because @ @ the filter. So , uh , so  that 's what  that was just the first stage of Wiener filtering that I tried.


So  but the thing is that you have to estimate the SF from the noisy spectrum , what you have. So you estimate the NF from the initial noise portions and then you subtract that from the current noisy spectrum to get an estimate of the SF. So sometimes that becomes zero because you do you don't have a true estimate of the noise. So the f filter will have like sometimes zeros in it

because some frequency values will be zeroed out because of that. And that creates a lot of discontinuities across the spectrum because @ @ the filter. So , uh , so  that 's what  that was just the first stage of Wiener filtering that I tried.

So is this , um , basically s uh , similar to just regular spectral subtraction ?


because some frequency values will be zeroed out because of that. And that creates a lot of discontinuities across the spectrum because @ @ the filter. So , uh , so  that 's what  that was just the first stage of Wiener filtering that I tried.

So is this , um , basically s uh , similar to just regular spectral subtraction ?


So is this , um , basically s uh , similar to just regular spectral subtraction ?


yeah. It 's  it 's  there 's a di there 's a whole class of techniques where you try in some sense to minimize the noise.


yeah. It 's  it 's  there 's a di there 's a whole class of techniques where you try in some sense to minimize the noise.

And it 's typically a mean square sense , uh  uh  uh , i in  in  in some way. And , uh  uh , spectral subtraction is  is , uh  uh , one approach to it.


yeah. It 's  it 's  there 's a di there 's a whole class of techniques where you try in some sense to minimize the noise.

And it 's typically a mean square sense , uh  uh  uh , i in  in  in some way. And , uh  uh , spectral subtraction is  is , uh  uh , one approach to it.

Do people use the Wiener filtering in combination with the spectral subtraction typically , or is i are they sort of competing techniques ?


And it 's typically a mean square sense , uh  uh  uh , i in  in  in some way. And , uh  uh , spectral subtraction is  is , uh  uh , one approach to it.

Do people use the Wiener filtering in combination with the spectral subtraction typically , or is i are they sort of competing techniques ?

Not seen. They are very s similar techniques.


Do people use the Wiener filtering in combination with the spectral subtraction typically , or is i are they sort of competing techniques ?

Not seen. They are very s similar techniques.

So it 's like I haven't seen anybody using s Wiener filter with spectral subtraction.


Not seen. They are very s similar techniques.

So it 's like I haven't seen anybody using s Wiener filter with spectral subtraction.


So it 's like I haven't seen anybody using s Wiener filter with spectral subtraction.


So it 's like I haven't seen anybody using s Wiener filter with spectral subtraction.

I mean , in the long run you 're doing the same thing


I mean , in the long run you 're doing the same thing


I mean , in the long run you 're doing the same thing

but y but there you make different approximations , and  in spectral subtraction , for instance , there 's a  a  an estimation factor.


but y but there you make different approximations , and  in spectral subtraction , for instance , there 's a  a  an estimation factor.


but y but there you make different approximations , and  in spectral subtraction , for instance , there 's a  a  an estimation factor.

You sometimes will figure out what the noise is and you 'll multiply that noise spectrum times some constant and subtract that rather than  and sometimes people  even though this really should be in the power domain , sometimes people s work in the magnitude domain because it  it  it works better.


You sometimes will figure out what the noise is and you 'll multiply that noise spectrum times some constant and subtract that rather than  and sometimes people  even though this really should be in the power domain , sometimes people s work in the magnitude domain because it  it  it works better.


You sometimes will figure out what the noise is and you 'll multiply that noise spectrum times some constant and subtract that rather than  and sometimes people  even though this really should be in the power domain , sometimes people s work in the magnitude domain because it  it  it works better.

So why did you choose , uh , Wiener filtering over some other  one of these other techniques ?


So why did you choose , uh , Wiener filtering over some other  one of these other techniques ?

Uh , the reason was , like , we had this choice of using spectral subtraction , Wiener filtering , and there was one more thing which I which I 'm trying , is this sub space approach. So , Stephane is working on spectral subtraction.


Uh , the reason was , like , we had this choice of using spectral subtraction , Wiener filtering , and there was one more thing which I which I 'm trying , is this sub space approach. So , Stephane is working on spectral subtraction.


we just wanted to have a few noise production  compensation techniques


I m I mean  yeah , I mean , there 's Car - Carmen 's working on another , on the vector Taylor series.


I m I mean  yeah , I mean , there 's Car - Carmen 's working on another , on the vector Taylor series.

So they were just kind of trying to cover a bunch of different things with this task and see , you know , what are  what are the issues for each of them.


So they were just kind of trying to cover a bunch of different things with this task and see , you know , what are  what are the issues for each of them.


So  so one of  one of the things that I tried , like I said , was to remove those zeros in the fri filter by doing some smoothing of the filter.


So  so one of  one of the things that I tried , like I said , was to remove those zeros in the fri filter by doing some smoothing of the filter.

Like , you estimate the edge of square and then you do a f smoothing across the frequency so that those zeros get , like , flattened out.


Like , you estimate the edge of square and then you do a f smoothing across the frequency so that those zeros get , like , flattened out.


Like , you estimate the edge of square and then you do a f smoothing across the frequency so that those zeros get , like , flattened out.

And that doesn't seems to be improving by trying it on the first time. So what I did was like I p did this and then you  I plugged in the  one more  the same thing but with the smoothed filter the second time.


And that doesn't seems to be improving by trying it on the first time. So what I did was like I p did this and then you  I plugged in the  one more  the same thing but with the smoothed filter the second time.


So that 's where I got like fifty - six point five percent improvement on SpeechDat - Car with that. And  So the other thing what I tried was I used still the ten frames of noise estimate but I used this channel zero VAD to drop the frames. So I 'm not  still not estimating. And that has taken the performance to like sixty - seven percent in SpeechDat - Car , which is  which  which like sort of shows that by using a proper VAD you can just take it to further , better levels. And  So.


So that 's where I got like fifty - six point five percent improvement on SpeechDat - Car with that. And  So the other thing what I tried was I used still the ten frames of noise estimate but I used this channel zero VAD to drop the frames. So I 'm not  still not estimating. And that has taken the performance to like sixty - seven percent in SpeechDat - Car , which is  which  which like sort of shows that by using a proper VAD you can just take it to further , better levels. And  So.

So that 's sort of like , you know , best - case performance ?


So that 's where I got like fifty - six point five percent improvement on SpeechDat - Car with that. And  So the other thing what I tried was I used still the ten frames of noise estimate but I used this channel zero VAD to drop the frames. So I 'm not  still not estimating. And that has taken the performance to like sixty - seven percent in SpeechDat - Car , which is  which  which like sort of shows that by using a proper VAD you can just take it to further , better levels. And  So.

So that 's sort of like , you know , best - case performance ?

Yeah , so far I 've seen sixty - seven  I mean , no , I haven't seen s like sixty - seven percent. And , uh , using the channel zero VAD to estimate the noise also seems to be improving but I don't have the results for all the cases with that. So I used channel zero VAD to estimate noise as a lesser 2 x frame , which is like ,  everywhere I use the channel zero VAD. And that seems to be the best combination , uh , rather than using a few frames to estimate and then drop a channel.


So that 's where I got like fifty - six point five percent improvement on SpeechDat - Car with that. And  So the other thing what I tried was I used still the ten frames of noise estimate but I used this channel zero VAD to drop the frames. So I 'm not  still not estimating. And that has taken the performance to like sixty - seven percent in SpeechDat - Car , which is  which  which like sort of shows that by using a proper VAD you can just take it to further , better levels. And  So.

So that 's sort of like , you know , best - case performance ?

Yeah , so far I 've seen sixty - seven  I mean , no , I haven't seen s like sixty - seven percent. And , uh , using the channel zero VAD to estimate the noise also seems to be improving but I don't have the results for all the cases with that. So I used channel zero VAD to estimate noise as a lesser 2 x frame , which is like ,  everywhere I use the channel zero VAD. And that seems to be the best combination , uh , rather than using a few frames to estimate and then drop a channel.

So I 'm  I 'm still a little confused. Is that channel zero information going to be accessible during this test.


So that 's sort of like , you know , best - case performance ?

Yeah , so far I 've seen sixty - seven  I mean , no , I haven't seen s like sixty - seven percent. And , uh , using the channel zero VAD to estimate the noise also seems to be improving but I don't have the results for all the cases with that. So I used channel zero VAD to estimate noise as a lesser 2 x frame , which is like ,  everywhere I use the channel zero VAD. And that seems to be the best combination , uh , rather than using a few frames to estimate and then drop a channel.

So I 'm  I 'm still a little confused. Is that channel zero information going to be accessible during this test.

Nnn , no. This is just to test whether we can really improve by using a better VAD.


Yeah , so far I 've seen sixty - seven  I mean , no , I haven't seen s like sixty - seven percent. And , uh , using the channel zero VAD to estimate the noise also seems to be improving but I don't have the results for all the cases with that. So I used channel zero VAD to estimate noise as a lesser 2 x frame , which is like ,  everywhere I use the channel zero VAD. And that seems to be the best combination , uh , rather than using a few frames to estimate and then drop a channel.

So I 'm  I 'm still a little confused. Is that channel zero information going to be accessible during this test.

Nnn , no. This is just to test whether we can really improve by using a better VAD.


So I 'm  I 'm still a little confused. Is that channel zero information going to be accessible during this test.

Nnn , no. This is just to test whether we can really improve by using a better VAD.


Nnn , no. This is just to test whether we can really improve by using a better VAD.


I mean  So this is like the noise compensation f is fixed


I mean  So this is like the noise compensation f is fixed

but you make a better decision on the endpoints. That 's , like  seems to be


but you make a better decision on the endpoints. That 's , like  seems to be

so we c so I mean , which  which means , like , by using this technique what we improve just the VAD


so we c so I mean , which  which means , like , by using this technique what we improve just the VAD

we can just take the performance by another ten percent or better.


we can just take the performance by another ten percent or better.

So , that  that was just the , uh , reason for doing that experiment. And , w um  Yeah , but this  all these things , I have to still try it on the TI - digits , which is like I 'm just running. And there seems to be not improving a  a lot on the TI - digits , so I 'm like investigating that , why it 's not. And , um , um  Well after that. So , uh  so the other  the other thing is  like I 've been  I 'm doing all this stuff on the power spectrum. So  Tried this stuff on the mel as well  mel and the magnitude , and mel magnitude , and all those things. But it seems to be the power spectrum seems to be getting the best result. So , one of  one of reasons I thought like doing the averaging , after the filtering using the mel filter bank , that seems to be maybe helping rather than trying it on the mel filter ba filtered outputs.


So , that  that was just the , uh , reason for doing that experiment. And , w um  Yeah , but this  all these things , I have to still try it on the TI - digits , which is like I 'm just running. And there seems to be not improving a  a lot on the TI - digits , so I 'm like investigating that , why it 's not. And , um , um  Well after that. So , uh  so the other  the other thing is  like I 've been  I 'm doing all this stuff on the power spectrum. So  Tried this stuff on the mel as well  mel and the magnitude , and mel magnitude , and all those things. But it seems to be the power spectrum seems to be getting the best result. So , one of  one of reasons I thought like doing the averaging , after the filtering using the mel filter bank , that seems to be maybe helping rather than trying it on the mel filter ba filtered outputs.


Yeah , th that 's  that 's the only thing that I could think of why  why it 's giving improvement on the mel. And , yep. So that 's it.


Yeah , th that 's  that 's the only thing that I could think of why  why it 's giving improvement on the mel. And , yep. So that 's it.

Subspace ,  I 'm  I 'm like  that 's still in  a little bit in the back burner because I 've been p putting a lot effort on this to make it work , on tuning things and other stuff.


Subspace ,  I 'm  I 'm like  that 's still in  a little bit in the back burner because I 've been p putting a lot effort on this to make it work , on tuning things and other stuff.

So I was like going parallely but not much of improvement. I 'm just  have some skeletons ready , need some more time for it.


So I was like going parallely but not much of improvement. I 'm just  have some skeletons ready , need some more time for it.


Uh , yeah. So ,  I 've been , uh , working still on the spectral subtraction. Um , So to r to remind you   a little bit of  of what I did before , is just  to apply some spectral subtraction with an overestimation factor also to get , um , an estimate of the noise , uh , spectrum , and subtract this estimation of the noise spectrum from the , uh , signal spectrum ,  but subtracting more when the SNR is  is , uh , low , which is a technique that it 's often used.


Uh , yeah. So ,  I 've been , uh , working still on the spectral subtraction. Um , So to r to remind you   a little bit of  of what I did before , is just  to apply some spectral subtraction with an overestimation factor also to get , um , an estimate of the noise , uh , spectrum , and subtract this estimation of the noise spectrum from the , uh , signal spectrum ,  but subtracting more when the SNR is  is , uh , low , which is a technique that it 's often used.

So you overestimate the noise spectrum. You multiply the noise spectrum by a factor , uh , which depends on the SNR.


So you overestimate the noise spectrum. You multiply the noise spectrum by a factor , uh , which depends on the SNR.

So , above twenty DB , it 's one , so you just subtract the noise.


So , above twenty DB , it 's one , so you just subtract the noise.

And then it 's b Generally  Well , I use , actually , a linear , uh , function of the SNR ,


And then it 's b Generally  Well , I use , actually , a linear , uh , function of the SNR ,

which is bounded to , like , two or three ,  when the SNR is below zero DB.


which is bounded to , like , two or three ,  when the SNR is below zero DB.

Um , doing just this , uh , either on the FFT bins or on the mel bands , um , t doesn't yield any improvement


which is bounded to , like , two or three ,  when the SNR is below zero DB.

Um , doing just this , uh , either on the FFT bins or on the mel bands , um , t doesn't yield any improvement

Oh ! Um , uh , what are you doing with negative , uh , powers ?


Um , doing just this , uh , either on the FFT bins or on the mel bands , um , t doesn't yield any improvement

Oh ! Um , uh , what are you doing with negative , uh , powers ?

o Yeah. So there is also a threshold , of course , because after subtraction you can have negative energies ,


Oh ! Um , uh , what are you doing with negative , uh , powers ?

o Yeah. So there is also a threshold , of course , because after subtraction you can have negative energies ,

and  So what I  I just do is to put , uh  to  to add  to put the threshold first and then to add a small amount of noise , which right now is speech - shaped. Um


o Yeah. So there is also a threshold , of course , because after subtraction you can have negative energies ,

and  So what I  I just do is to put , uh  to  to add  to put the threshold first and then to add a small amount of noise , which right now is speech - shaped. Um


and  So what I  I just do is to put , uh  to  to add  to put the threshold first and then to add a small amount of noise , which right now is speech - shaped. Um

Yeah , so it 's  a it has the overall  overall energy , uh  pow it has the overall power spectrum of speech. So with a bump around one kilohertz.


and  So what I  I just do is to put , uh  to  to add  to put the threshold first and then to add a small amount of noise , which right now is speech - shaped. Um

Yeah , so it 's  a it has the overall  overall energy , uh  pow it has the overall power spectrum of speech. So with a bump around one kilohertz.

So when y when you talk about there being something less than zero after subtracting the noise , is that at a particular frequency bin ?


Yeah , so it 's  a it has the overall  overall energy , uh  pow it has the overall power spectrum of speech. So with a bump around one kilohertz.

So when y when you talk about there being something less than zero after subtracting the noise , is that at a particular frequency bin ?


So when y when you talk about there being something less than zero after subtracting the noise , is that at a particular frequency bin ?

There can be frequency bins with negative values.


There can be frequency bins with negative values.

And so when you say you 're adding something that has the overall shape of speech , is that in a  in a particular frequency bin ? Or you 're adding something across all the frequencies when you get these negatives ?


There can be frequency bins with negative values.

And so when you say you 're adding something that has the overall shape of speech , is that in a  in a particular frequency bin ? Or you 're adding something across all the frequencies when you get these negatives ?

For each frequencies I a I 'm adding some , uh , noise , but the a the amount of  the amount of noise I add is not the same for all the frequency bins.


And so when you say you 're adding something that has the overall shape of speech , is that in a  in a particular frequency bin ? Or you 're adding something across all the frequencies when you get these negatives ?

For each frequencies I a I 'm adding some , uh , noise , but the a the amount of  the amount of noise I add is not the same for all the frequency bins.

Uh. Right now I don't think if it makes sense to add something that 's speech - shaped , because then you have silence portion that have some spectra similar to the sp the overall speech spectra.


For each frequencies I a I 'm adding some , uh , noise , but the a the amount of  the amount of noise I add is not the same for all the frequency bins.

Uh. Right now I don't think if it makes sense to add something that 's speech - shaped , because then you have silence portion that have some spectra similar to the sp the overall speech spectra.


Uh. Right now I don't think if it makes sense to add something that 's speech - shaped , because then you have silence portion that have some spectra similar to the sp the overall speech spectra.

But  Yeah. So this is something I can still work on ,


But  Yeah. So this is something I can still work on ,


But  Yeah. So this is something I can still work on ,

I 'm trying to understand what it means when you do the spectral subtraction and you get a negative. It means that at that particular frequency range you subtracted more energy than there was actually


I 'm trying to understand what it means when you do the spectral subtraction and you get a negative. It means that at that particular frequency range you subtracted more energy than there was actually

That means that  Mm - hmm. Yeah. So  so yeah , you have an  an estimation of the noise spectrum , but sometimes , of course , it 's  as the noise is not perfectly stationary , sometimes this estimation can be , uh , too small , so you don't subtract enough. But sometimes it can be too large also. If  if the noise , uh , energy in this particular frequency band drops for some reason.


That means that  Mm - hmm. Yeah. So  so yeah , you have an  an estimation of the noise spectrum , but sometimes , of course , it 's  as the noise is not perfectly stationary , sometimes this estimation can be , uh , too small , so you don't subtract enough. But sometimes it can be too large also. If  if the noise , uh , energy in this particular frequency band drops for some reason.

So in  in an ideal word i world  if the noise were always the same , then , when you subtracted it the worst that i you would get would be a zero. I mean , the lowest you would get would be a zero , cuz i if there was no other energy there you 're just subtracting exactly the noise.


So in  in an ideal word i world  if the noise were always the same , then , when you subtracted it the worst that i you would get would be a zero. I mean , the lowest you would get would be a zero , cuz i if there was no other energy there you 're just subtracting exactly the noise.


So in  in an ideal word i world  if the noise were always the same , then , when you subtracted it the worst that i you would get would be a zero. I mean , the lowest you would get would be a zero , cuz i if there was no other energy there you 're just subtracting exactly the noise.

Yep , there 's all  there 's all sorts of , uh , deviations from the ideal here.


Yep , there 's all  there 's all sorts of , uh , deviations from the ideal here.


Yep , there 's all  there 's all sorts of , uh , deviations from the ideal here.

I mean , for instance , you 're  you 're talking about the signal and noise , um , at a particular point. And even if something is sort of stationary in ster terms of statistics , there 's no guarantee that any particular instantiation or piece of it is exactly a particular number or bounded by a particular range.


I mean , for instance , you 're  you 're talking about the signal and noise , um , at a particular point. And even if something is sort of stationary in ster terms of statistics , there 's no guarantee that any particular instantiation or piece of it is exactly a particular number or bounded by a particular range.

So , you 're figuring out from some chunk of  of  of the signal what you think the noise is. Then you 're subtracting that from another chunk ,


So , you 're figuring out from some chunk of  of  of the signal what you think the noise is. Then you 're subtracting that from another chunk ,

and there 's absolutely no reason to think that you 'd know that it wouldn't , uh , be negative in some places.


and there 's absolutely no reason to think that you 'd know that it wouldn't , uh , be negative in some places.

Uh , on the other hand that just means that in some sense you 've made a mistake because you certainly have stra subtracted a bigger number than is due to the noise.


Uh , on the other hand that just means that in some sense you 've made a mistake because you certainly have stra subtracted a bigger number than is due to the noise.

Um  Also , we speak  the whole  where all this stuff comes from is from an assumption that signal and noise are uncorrelated. And that certainly makes sense in s in  in a statistical interpretation , that , you know , over , um , all possible realizations that they 're uncorrelated


Um  Also , we speak  the whole  where all this stuff comes from is from an assumption that signal and noise are uncorrelated. And that certainly makes sense in s in  in a statistical interpretation , that , you know , over , um , all possible realizations that they 're uncorrelated

or assuming , uh , ergodicity that i that i um , across time , uh , it 's uncorrelated. But if you just look at  a quarter second , uh , and you cross - multiply the two things , uh , you could very well , uh , end up with something that sums to something that 's not zero. So in fact , the two signals could have some relation to one another. And so there 's all sorts of deviations from ideal in this. And  and given all that , you could definitely end up with something that 's negative. But if down the road you 're making use of something as if it is a power spectrum , um , then it can be bad to have something negative. Now , the other thing I wonder about actually is , what if you left it negative ? What happens ?


or assuming , uh , ergodicity that i that i um , across time , uh , it 's uncorrelated. But if you just look at  a quarter second , uh , and you cross - multiply the two things , uh , you could very well , uh , end up with something that sums to something that 's not zero. So in fact , the two signals could have some relation to one another. And so there 's all sorts of deviations from ideal in this. And  and given all that , you could definitely end up with something that 's negative. But if down the road you 're making use of something as if it is a power spectrum , um , then it can be bad to have something negative. Now , the other thing I wonder about actually is , what if you left it negative ? What happens ?

I mean , because  Um , are you taking the log before you add them up to the mel ?


I mean , because  Um , are you taking the log before you add them up to the mel ?

Right. So the thing is , I wonder how  if you put your thresholds after that , I wonder how often you would end up with , uh  with negative values.


I mean , because  Um , are you taking the log before you add them up to the mel ?

Right. So the thing is , I wonder how  if you put your thresholds after that , I wonder how often you would end up with , uh  with negative values.

But you will  But you end up reducing some neighboring frequency bins  @ @ in the average , right ? When you add the negative to the positive value which is the true estimate.


Right. So the thing is , I wonder how  if you put your thresholds after that , I wonder how often you would end up with , uh  with negative values.

But you will  But you end up reducing some neighboring frequency bins  @ @ in the average , right ? When you add the negative to the positive value which is the true estimate.

Yeah. But nonetheless , uh , you know , these are  it 's another f kind of smoothing , right ? that you 're doing.


But you will  But you end up reducing some neighboring frequency bins  @ @ in the average , right ? When you add the negative to the positive value which is the true estimate.

Yeah. But nonetheless , uh , you know , these are  it 's another f kind of smoothing , right ? that you 're doing.

Right. So , you 've done your best shot at figuring out what the noise should be , and now i then you 've subtracted it off. And then after that , instead of  instead of , uh , uh , leaving it as is and adding things  adding up some neighbors , you artificially push it up.


Yeah. But nonetheless , uh , you know , these are  it 's another f kind of smoothing , right ? that you 're doing.

Right. So , you 've done your best shot at figuring out what the noise should be , and now i then you 've subtracted it off. And then after that , instead of  instead of , uh , uh , leaving it as is and adding things  adding up some neighbors , you artificially push it up.


Right. So , you 've done your best shot at figuring out what the noise should be , and now i then you 've subtracted it off. And then after that , instead of  instead of , uh , uh , leaving it as is and adding things  adding up some neighbors , you artificially push it up.

Which is , you know , it 's  there 's no particular reason that that 's the right thing to do either , right ?


Which is , you know , it 's  there 's no particular reason that that 's the right thing to do either , right ?

So , um , uh , i in fact , what you 'd be doing is saying , " well , we 're d we 're  we 're going to definitely diminish the effect of this frequency in this little frequency bin in the  in the overall mel summation ". It 's just a thought. I d I don't know if it would be


Which is , you know , it 's  there 's no particular reason that that 's the right thing to do either , right ?

So , um , uh , i in fact , what you 'd be doing is saying , " well , we 're d we 're  we 're going to definitely diminish the effect of this frequency in this little frequency bin in the  in the overall mel summation ". It 's just a thought. I d I don't know if it would be

Sort of the opposite of that would be if  if you find out you 're going to get a negative number , you don't do the subtraction for that bin.


So , um , uh , i in fact , what you 'd be doing is saying , " well , we 're d we 're  we 're going to definitely diminish the effect of this frequency in this little frequency bin in the  in the overall mel summation ". It 's just a thought. I d I don't know if it would be

Sort of the opposite of that would be if  if you find out you 're going to get a negative number , you don't do the subtraction for that bin.


Sort of the opposite of that would be if  if you find out you 're going to get a negative number , you don't do the subtraction for that bin.


That would be almost the opposite , right ? Instead of leaving it negative , you don't do it. If your  if your subtraction 's going to result in a negative number , you  you don't do subtraction in that.


That would be almost the opposite , right ? Instead of leaving it negative , you don't do it. If your  if your subtraction 's going to result in a negative number , you  you don't do subtraction in that.

Yeah , but that means that in a situation where you thought that  that the bin was almost entirely noise , you left it.


That would be almost the opposite , right ? Instead of leaving it negative , you don't do it. If your  if your subtraction 's going to result in a negative number , you  you don't do subtraction in that.

Yeah , but that means that in a situation where you thought that  that the bin was almost entirely noise , you left it.

Yeah. Yeah , I 'm just saying that 's like the opposite.


Yeah , but that means that in a situation where you thought that  that the bin was almost entirely noise , you left it.

Yeah. Yeah , I 'm just saying that 's like the opposite.


Yeah. Yeah , I 'm just saying that 's like the opposite.


Well , yeah that 's  that 's the opposite ,


Well , yeah that 's  that 's the opposite ,

And , yeah , some people also  if it 's a negative value they , uh , re - compute it using inter interpolation from the edges and bins.


And , yeah , some people also  if it 's a negative value they , uh , re - compute it using inter interpolation from the edges and bins.


And , yeah , some people also  if it 's a negative value they , uh , re - compute it using inter interpolation from the edges and bins.

Well , there are different things that you can do.


Well , there are different things that you can do.


Well , there are different things that you can do.

People can also , uh , reflect it back up and essentially do a full wave rectification instead of a  instead of half wave.


People can also , uh , reflect it back up and essentially do a full wave rectification instead of a  instead of half wave.

But it was just a thought that  that it might be something to try.


People can also , uh , reflect it back up and essentially do a full wave rectification instead of a  instead of half wave.

But it was just a thought that  that it might be something to try.

Mm - hmm. Mm - hmm. Yep. Well , actually I tried ,  something else based on this , um , is to  to put some smoothing , um , because it seems to  to help or it seems to help the Wiener filtering


But it was just a thought that  that it might be something to try.

Mm - hmm. Mm - hmm. Yep. Well , actually I tried ,  something else based on this , um , is to  to put some smoothing , um , because it seems to  to help or it seems to help the Wiener filtering


But it was just a thought that  that it might be something to try.

Mm - hmm. Mm - hmm. Yep. Well , actually I tried ,  something else based on this , um , is to  to put some smoothing , um , because it seems to  to help or it seems to help the Wiener filtering

and , mmm  So what I did is , uh , some kind of nonlinear smoothing. Actually I have a recursion that computes  Yeah , let me go back a little bit. Actually , when you do spectral subtraction you can , uh , find this  this equivalent in the s in the spectral domain. You can uh compute , y you can say that d your spectral subtraction is a filter , um , and the gain of this filter is the , um ,  signal energy minus what you subtract , divided by the signal energy. And this is a gain that varies over time , and , you know , of course , uh , depending on the s on the noise spectrum and on the speech spectrum. And  what happen actually is that during low SNR values , the gain is close to zero but it varies a lot. Mmm , and this  this is the cause of musical noise and all these  the   the fact you  we go below zero one frame and then you can have an energy that 's above zero.


Mm - hmm. Mm - hmm. Yep. Well , actually I tried ,  something else based on this , um , is to  to put some smoothing , um , because it seems to  to help or it seems to help the Wiener filtering

and , mmm  So what I did is , uh , some kind of nonlinear smoothing. Actually I have a recursion that computes  Yeah , let me go back a little bit. Actually , when you do spectral subtraction you can , uh , find this  this equivalent in the s in the spectral domain. You can uh compute , y you can say that d your spectral subtraction is a filter , um , and the gain of this filter is the , um ,  signal energy minus what you subtract , divided by the signal energy. And this is a gain that varies over time , and , you know , of course , uh , depending on the s on the noise spectrum and on the speech spectrum. And  what happen actually is that during low SNR values , the gain is close to zero but it varies a lot. Mmm , and this  this is the cause of musical noise and all these  the   the fact you  we go below zero one frame and then you can have an energy that 's above zero.


and , mmm  So what I did is , uh , some kind of nonlinear smoothing. Actually I have a recursion that computes  Yeah , let me go back a little bit. Actually , when you do spectral subtraction you can , uh , find this  this equivalent in the s in the spectral domain. You can uh compute , y you can say that d your spectral subtraction is a filter , um , and the gain of this filter is the , um ,  signal energy minus what you subtract , divided by the signal energy. And this is a gain that varies over time , and , you know , of course , uh , depending on the s on the noise spectrum and on the speech spectrum. And  what happen actually is that during low SNR values , the gain is close to zero but it varies a lot. Mmm , and this  this is the cause of musical noise and all these  the   the fact you  we go below zero one frame and then you can have an energy that 's above zero.

And  Mmm. So the smoothing is  I did a smoothing actually on this gain , uh , trajectory. But it 's  the smoothing is nonlinear in the sense that I tried to not smooth if the gain is high , because in this case we know that , uh , the estimate of the gain is correct because we  we are not close to  to  to zero , um , and to do more smoothing if the gain is low. Mmm. Um. Yeah. So , well , basically that 's this idea , and it seems to give pretty good results , uh , although I 've just  just tested on Italian and Finnish. And on Italian it seems  my result seems to be a little bit better than the Wiener filtering ,


and , mmm  So what I did is , uh , some kind of nonlinear smoothing. Actually I have a recursion that computes  Yeah , let me go back a little bit. Actually , when you do spectral subtraction you can , uh , find this  this equivalent in the s in the spectral domain. You can uh compute , y you can say that d your spectral subtraction is a filter , um , and the gain of this filter is the , um ,  signal energy minus what you subtract , divided by the signal energy. And this is a gain that varies over time , and , you know , of course , uh , depending on the s on the noise spectrum and on the speech spectrum. And  what happen actually is that during low SNR values , the gain is close to zero but it varies a lot. Mmm , and this  this is the cause of musical noise and all these  the   the fact you  we go below zero one frame and then you can have an energy that 's above zero.

And  Mmm. So the smoothing is  I did a smoothing actually on this gain , uh , trajectory. But it 's  the smoothing is nonlinear in the sense that I tried to not smooth if the gain is high , because in this case we know that , uh , the estimate of the gain is correct because we  we are not close to  to  to zero , um , and to do more smoothing if the gain is low. Mmm. Um. Yeah. So , well , basically that 's this idea , and it seems to give pretty good results , uh , although I 've just  just tested on Italian and Finnish. And on Italian it seems  my result seems to be a little bit better than the Wiener filtering ,

Mm - hmm. Yeah , the one you showed yesterday.


And  Mmm. So the smoothing is  I did a smoothing actually on this gain , uh , trajectory. But it 's  the smoothing is nonlinear in the sense that I tried to not smooth if the gain is high , because in this case we know that , uh , the estimate of the gain is correct because we  we are not close to  to  to zero , um , and to do more smoothing if the gain is low. Mmm. Um. Yeah. So , well , basically that 's this idea , and it seems to give pretty good results , uh , although I 've just  just tested on Italian and Finnish. And on Italian it seems  my result seems to be a little bit better than the Wiener filtering ,

Mm - hmm. Yeah , the one you showed yesterday.


Mm - hmm. Yeah , the one you showed yesterday.


Uh , I don't know if you have these improvement the detailed improvements for Italian , Finnish , and Spanish there


Uh , I don't know if you have these improvement the detailed improvements for Italian , Finnish , and Spanish there

I  I just  just have the final number here.


I  I just  just have the final number here.


I  I just  just have the final number here.

So these numbers he was giving before with the four point three , and the ten point one , and so forth , those were Italian , right ?


So these numbers he was giving before with the four point three , and the ten point one , and so forth , those were Italian , right ?


I actually didn't give you the number which is the final one ,


I actually didn't give you the number which is the final one ,

which is , after two stages of Wiener filtering. I mean , that was I just  well , like the overall improvement is like fifty - six point five. So ,


which is , after two stages of Wiener filtering. I mean , that was I just  well , like the overall improvement is like fifty - six point five. So ,


which is , after two stages of Wiener filtering. I mean , that was I just  well , like the overall improvement is like fifty - six point five. So ,

I mean , his number is still better than what I got in the two stages of Wiener filtering.


I mean , his number is still better than what I got in the two stages of Wiener filtering.


I mean , his number is still better than what I got in the two stages of Wiener filtering.

On Italian. But on Finnish it 's a little bit worse , apparently.


On Italian. But on Finnish it 's a little bit worse , apparently.


On Italian. But on Finnish it 's a little bit worse , apparently.

But do you have numbers in terms of word error rates on  on Italian ? So just so you have some sense of reference ?


But do you have numbers in terms of word error rates on  on Italian ? So just so you have some sense of reference ?

Yeah. Uh , so , it 's , uh , three point , uh , eight.


Yeah. Uh , so , it 's , uh , three point , uh , eight.


And then , uh , d uh , nine point , uh , one.


And then , uh , d uh , nine point , uh , one.

And this is , um , spectral subtraction plus what ?


And this is , um , spectral subtraction plus what ?

Plus  plus nonlinear smoothing. Well , it 's  the system  it 's exactly the sys the same system as Sunil tried ,


Plus  plus nonlinear smoothing. Well , it 's  the system  it 's exactly the sys the same system as Sunil tried ,


Yeah. But instead of double stage Wiener filtering , it 's  it 's this smoothed spectral subtraction. Um , yeah.


Yeah. But instead of double stage Wiener filtering , it 's  it 's this smoothed spectral subtraction. Um , yeah.

What is it the , um , France Telecom system uses


Yeah. But instead of double stage Wiener filtering , it 's  it 's this smoothed spectral subtraction. Um , yeah.

What is it the , um , France Telecom system uses

for  Do they use spectral subtraction , or Wiener filtering , or  ?


What is it the , um , France Telecom system uses

for  Do they use spectral subtraction , or Wiener filtering , or  ?


for  Do they use spectral subtraction , or Wiener filtering , or  ?


Well , it 's some kind of Wiener filtering


Well , it 's some kind of Wiener filtering

Yeah , filtering. Yeah , it 's not exactly Wiener filtering but some variant of Wiener filtering.


Yeah , filtering. Yeah , it 's not exactly Wiener filtering but some variant of Wiener filtering.


Yeah , plus , uh , I guess they have some sort of cepstral normalization , as well.


Yeah , plus , uh , I guess they have some sort of cepstral normalization , as well.

s They have like  yeah , th the  just noise compensation technique is a variant of Wiener filtering ,


Yeah , plus , uh , I guess they have some sort of cepstral normalization , as well.

s They have like  yeah , th the  just noise compensation technique is a variant of Wiener filtering ,

plus they do some  some smoothing techniques on the final filter. The  th they actually do the filtering in the time domain.


s They have like  yeah , th the  just noise compensation technique is a variant of Wiener filtering ,

plus they do some  some smoothing techniques on the final filter. The  th they actually do the filtering in the time domain.


plus they do some  some smoothing techniques on the final filter. The  th they actually do the filtering in the time domain.


So they would take this HF squared back , taking inverse Fourier transform. And they convolve the time domain signal with that.


So they would take this HF squared back , taking inverse Fourier transform. And they convolve the time domain signal with that.

And they do some smoothing on that final filter , impulse response.


And they do some smoothing on that final filter , impulse response.

But they also have two  two different smoothing @ @.


But they also have two  two different smoothing @ @.

One in the time domain and one in the frequency domain by just taking the first , um , coefficients of the impulse response.


One in the time domain and one in the frequency domain by just taking the first , um , coefficients of the impulse response.

So , basically it 's similar. I mean , what you did , it 's similar


So , basically it 's similar. I mean , what you did , it 's similar

because you have also two  two kind of smoothing.


because you have also two  two kind of smoothing.

One in the time domain , and one in the frequency domain ,


One in the time domain , and one in the frequency domain ,


One in the time domain , and one in the frequency domain ,

Does the smoothing in the time domain help


Does the smoothing in the time domain help


Does the smoothing in the time domain help

Well , do you get this musical noise stuff with Wiener filtering or is that only with , uh , spectral subtraction ?


Does the smoothing in the time domain help

Well , do you get this musical noise stuff with Wiener filtering or is that only with , uh , spectral subtraction ?

No , you get it with Wiener filtering also.


Well , do you get this musical noise stuff with Wiener filtering or is that only with , uh , spectral subtraction ?

No , you get it with Wiener filtering also.


Well , do you get this musical noise stuff with Wiener filtering or is that only with , uh , spectral subtraction ?

No , you get it with Wiener filtering also.

Does the smoothing in the time domain help with that ? Or some other smoothing ?


No , you get it with Wiener filtering also.

Does the smoothing in the time domain help with that ? Or some other smoothing ?

Oh , no , you still end up with zeros in the s spectrum. Sometimes.


Does the smoothing in the time domain help with that ? Or some other smoothing ?

Oh , no , you still end up with zeros in the s spectrum. Sometimes.


Oh , no , you still end up with zeros in the s spectrum. Sometimes.

I mean , it 's not clear that these musical noises hurt us in recognition.


I mean , it 's not clear that these musical noises hurt us in recognition.


But we 're not listening to it , usually.


But we 're not listening to it , usually.

Uh , actually the  the smoothing that I did  do here reduced the musical noise. Well , it


Uh , actually the  the smoothing that I did  do here reduced the musical noise. Well , it


Well , I cannot  you cannot hear beca well , actually what I d did not say is that this is not in the FFT bins. This is in the mel frequency bands. Um  So , it could be seen as a f a  a smoothing in the frequency domain because I used , in ad mel bands in addition and then the other phase of smoothing in the time domain. Mmm. But , when you look at the spectrogram , if you don't have an any smoothing , you clearly see , like  in silence portions , and at the beginning and end of speech , you see spots of high energy randomly distributed over the  the spectrogram.


yeah , if  if it  If you listen to it  uh , if you do this in the FFT bins , then you have spots of energy randomly distributing. And if you f if you re - synthesize these spot sounds as , like , sounds ,


yeah , if  if it  If you listen to it  uh , if you do this in the FFT bins , then you have spots of energy randomly distributing. And if you f if you re - synthesize these spot sounds as , like , sounds ,

Well , none of these systems , by the way , have  I mean , y you both are  are working with , um , our system that does not have the neural net ,


Well , none of these systems , by the way , have  I mean , y you both are  are working with , um , our system that does not have the neural net ,


OK. So one would hope , presumably , that the neural net part of it would  would improve things further as  as they did before.


OK. So one would hope , presumably , that the neural net part of it would  would improve things further as  as they did before.

Yeah. Yeah. Um  Yeah , although if  if we , um , look at the result from the proposals ,  one of the reason , uh , the n system with the neural net was , um , more than  well , around five percent better , is that it was much better on highly mismatched condition. I 'm thinking , for instance , on the TI - digits trained on clean speech and tested on noisy speech.


OK. So one would hope , presumably , that the neural net part of it would  would improve things further as  as they did before.

Yeah. Yeah. Um  Yeah , although if  if we , um , look at the result from the proposals ,  one of the reason , uh , the n system with the neural net was , um , more than  well , around five percent better , is that it was much better on highly mismatched condition. I 'm thinking , for instance , on the TI - digits trained on clean speech and tested on noisy speech.

Uh , for this case , the system with the neural net was much better.


Yeah. Yeah. Um  Yeah , although if  if we , um , look at the result from the proposals ,  one of the reason , uh , the n system with the neural net was , um , more than  well , around five percent better , is that it was much better on highly mismatched condition. I 'm thinking , for instance , on the TI - digits trained on clean speech and tested on noisy speech.

Uh , for this case , the system with the neural net was much better.


Uh , for this case , the system with the neural net was much better.

But not much on the  in the other cases.


But not much on the  in the other cases.

And if we have no , uh , spectral subtraction or Wiener filtering , um , i the system is  Uh , we thought the neural  neural network is much better than before , even in these cases of high mismatch. So , maybe the neural net will help less but , um


And if we have no , uh , spectral subtraction or Wiener filtering , um , i the system is  Uh , we thought the neural  neural network is much better than before , even in these cases of high mismatch. So , maybe the neural net will help less but , um

Could you train a neural net to do spectral subtraction ?


And if we have no , uh , spectral subtraction or Wiener filtering , um , i the system is  Uh , we thought the neural  neural network is much better than before , even in these cases of high mismatch. So , maybe the neural net will help less but , um

Could you train a neural net to do spectral subtraction ?

Yeah , it could do a nonlinear spectral subtraction


Could you train a neural net to do spectral subtraction ?

Yeah , it could do a nonlinear spectral subtraction


Could you train a neural net to do spectral subtraction ?

Yeah , it could do a nonlinear spectral subtraction

but I don't know if it  I mean , you have to figure out what your targets are.


Yeah , it could do a nonlinear spectral subtraction

but I don't know if it  I mean , you have to figure out what your targets are.

Yeah , I was thinking if you had a clean version of the signal and  and a noisy version , and your targets were the M F - uh , you know , whatever , frequency bins


but I don't know if it  I mean , you have to figure out what your targets are.

Yeah , I was thinking if you had a clean version of the signal and  and a noisy version , and your targets were the M F - uh , you know , whatever , frequency bins


Yeah , I was thinking if you had a clean version of the signal and  and a noisy version , and your targets were the M F - uh , you know , whatever , frequency bins


Yeah , well , that 's not so much spectral subtraction then ,


Yeah , well , that 's not so much spectral subtraction then ,

but  but  but it 's  but at any rate , yeah , people , uh


but  but  but it 's  but at any rate , yeah , people , uh

y yeah , in fact , we had visitors here who did that I think when you were here ba way back when.


y yeah , in fact , we had visitors here who did that I think when you were here ba way back when.


y yeah , in fact , we had visitors here who did that I think when you were here ba way back when.

Uh , people  d done lots of experimentation over the years with training neural nets. And it 's not a bad thing to do. It 's another approach.


Uh , people  d done lots of experimentation over the years with training neural nets. And it 's not a bad thing to do. It 's another approach.


The objection everyone always raises , which has some truth to it is that , um , it 's good for mapping from a particular noise to clean but then you get a different noise.


The objection everyone always raises , which has some truth to it is that , um , it 's good for mapping from a particular noise to clean but then you get a different noise.

And the experiments we saw that visitors did here showed that it  there was at least some , um ,   gentleness to the degradation when you switched to different noises. It did seem to help. So that  you 're right , that 's another  another way to go.


The objection everyone always raises , which has some truth to it is that , um , it 's good for mapping from a particular noise to clean but then you get a different noise.

And the experiments we saw that visitors did here showed that it  there was at least some , um ,   gentleness to the degradation when you switched to different noises. It did seem to help. So that  you 're right , that 's another  another way to go.

How did it compare on  I mean , for  for good cases where it  it  uh , stuff that it was trained on ? Did it do pretty well ?


And the experiments we saw that visitors did here showed that it  there was at least some , um ,   gentleness to the degradation when you switched to different noises. It did seem to help. So that  you 're right , that 's another  another way to go.

How did it compare on  I mean , for  for good cases where it  it  uh , stuff that it was trained on ? Did it do pretty well ?


How did it compare on  I mean , for  for good cases where it  it  uh , stuff that it was trained on ? Did it do pretty well ?


but to some extent that 's kind of what we 're doing. I mean , we 're not doing exactly that , we 're not trying to generate good examples but by trying to do the best classifier you possibly can , for these little phonetic categories ,


but to some extent that 's kind of what we 're doing. I mean , we 're not doing exactly that , we 're not trying to generate good examples but by trying to do the best classifier you possibly can , for these little phonetic categories ,

Mm - hmm. You could say it 's sort of built in.


but to some extent that 's kind of what we 're doing. I mean , we 're not doing exactly that , we 're not trying to generate good examples but by trying to do the best classifier you possibly can , for these little phonetic categories ,

Mm - hmm. You could say it 's sort of built in.

It 's  Yeah , it 's kind of built into that.


Mm - hmm. You could say it 's sort of built in.

It 's  Yeah , it 's kind of built into that.

And  and that 's why we have found that it  it does help.


It 's  Yeah , it 's kind of built into that.

And  and that 's why we have found that it  it does help.


And  and that 's why we have found that it  it does help.

Um  so , um , yeah , I mean , we 'll just have to try it. But I  I would  I would  I would imagine that it will help some. I mean , it  we 'll just have to see whether it helps more or less the same , but I would imagine it would help some.


Um  so , um , yeah , I mean , we 'll just have to try it. But I  I would  I would  I would imagine that it will help some. I mean , it  we 'll just have to see whether it helps more or less the same , but I would imagine it would help some.

So in any event , all of this  I was just confirming that all of this was with a simpler system.


So in any event , all of this  I was just confirming that all of this was with a simpler system.


So in any event , all of this  I was just confirming that all of this was with a simpler system.

yeah. Um , Yeah , so this is th the , um  Well , actually , this was kind of the first try with this spectral subtraction plus smoothing ,


yeah. Um , Yeah , so this is th the , um  Well , actually , this was kind of the first try with this spectral subtraction plus smoothing ,


yeah. Um , Yeah , so this is th the , um  Well , actually , this was kind of the first try with this spectral subtraction plus smoothing ,

and I was kind of excited by the result.


and I was kind of excited by the result.

Um , then I started to optimize the different parameters. And , uh , the first thing I tried to optimize is the , um , time constant of the smoothing. And it seems that the one that I chose for the first experiment was the optimal one , so  uh ,


Um , then I started to optimize the different parameters. And , uh , the first thing I tried to optimize is the , um , time constant of the smoothing. And it seems that the one that I chose for the first experiment was the optimal one , so  uh ,

Um , so this is the first thing. Um  Yeah , another thing that I  it 's important to mention is , um , that this has a this has some additional latency. Um. Because when I do the smoothing , uh , it 's a recursion that estimated the means , so  of the g of the gain curve. And this is a filter that has some latency. And I noticed that it 's better if we take into account this latency. So , instead o of using the current estimated mean to , uh , subtract the current frame , it 's better to use an estimate that 's some somewhere in the future. Um


Um , then I started to optimize the different parameters. And , uh , the first thing I tried to optimize is the , um , time constant of the smoothing. And it seems that the one that I chose for the first experiment was the optimal one , so  uh ,

Um , so this is the first thing. Um  Yeah , another thing that I  it 's important to mention is , um , that this has a this has some additional latency. Um. Because when I do the smoothing , uh , it 's a recursion that estimated the means , so  of the g of the gain curve. And this is a filter that has some latency. And I noticed that it 's better if we take into account this latency. So , instead o of using the current estimated mean to , uh , subtract the current frame , it 's better to use an estimate that 's some somewhere in the future. Um

And that 's what causes the latency ? OK.


Um , so this is the first thing. Um  Yeah , another thing that I  it 's important to mention is , um , that this has a this has some additional latency. Um. Because when I do the smoothing , uh , it 's a recursion that estimated the means , so  of the g of the gain curve. And this is a filter that has some latency. And I noticed that it 's better if we take into account this latency. So , instead o of using the current estimated mean to , uh , subtract the current frame , it 's better to use an estimate that 's some somewhere in the future. Um

And that 's what causes the latency ? OK.

You mean , the m the mean is computed o based on some frames in the future also ?


And that 's what causes the latency ? OK.

You mean , the m the mean is computed o based on some frames in the future also ?


You mean , the m the mean is computed o based on some frames in the future also ?


It 's the recursion , so it 's  it 's the center recursion , right ?


It 's the recursion , so it 's  it 's the center recursion , right ?

Um  and the latency of this recursion is around fifty milliseconds.


Um  and the latency of this recursion is around fifty milliseconds.


why  why is that delay coming ? Like , you estimate the mean ?


why  why is that delay coming ? Like , you estimate the mean ?

Yeah , the mean estimation has some delay , right ?


why  why is that delay coming ? Like , you estimate the mean ?

Yeah , the mean estimation has some delay , right ?

I mean , the  the filter that  that estimates the mean has a time constant.


Yeah , the mean estimation has some delay , right ?

I mean , the  the filter that  that estimates the mean has a time constant.

It isn't  OK , so it 's like it looks into the future also. OK.


I mean , the  the filter that  that estimates the mean has a time constant.

It isn't  OK , so it 's like it looks into the future also. OK.


It isn't  OK , so it 's like it looks into the future also. OK.

It 's , uh , not as good. It 's not bad.


It 's , uh , not as good. It 's not bad.


It 's , uh , not as good. It 's not bad.

Um , it helps a lot over the ba the baseline but , mmm


Um , it helps a lot over the ba the baseline but , mmm

it  It 's around three percent , um , relative.


it  It 's around three percent , um , relative.


It 's depending on how all this stuff comes out we may or may not be able to add any latency.


It 's depending on how all this stuff comes out we may or may not be able to add any latency.

Yeah , but  Yeah. So , yeah , it depends. Uh , y actually , it 's  it 's l it 's three percent. Right. Mmm. Yeah , b but I don't think we have to worry too much on that right now while  you kno. Mm - hmm.


It 's depending on how all this stuff comes out we may or may not be able to add any latency.

Yeah , but  Yeah. So , yeah , it depends. Uh , y actually , it 's  it 's l it 's three percent. Right. Mmm. Yeah , b but I don't think we have to worry too much on that right now while  you kno. Mm - hmm.

Um , s Yeah , I mean , I think the only thing is that


Yeah , but  Yeah. So , yeah , it depends. Uh , y actually , it 's  it 's l it 's three percent. Right. Mmm. Yeah , b but I don't think we have to worry too much on that right now while  you kno. Mm - hmm.

Um , s Yeah , I mean , I think the only thing is that


Um , s Yeah , I mean , I think the only thing is that


Because if we completely ignore latency , and then we discover that we really have to do something about it , we 're going to be  find ourselves in a bind.


Because if we completely ignore latency , and then we discover that we really have to do something about it , we 're going to be  find ourselves in a bind.

So , um , you know , maybe you could make it twenty - five. You know what I mean ?


So , um , you know , maybe you could make it twenty - five. You know what I mean ?

Yeah , just , you know , just be  be a little conservative


Yeah , just , you know , just be  be a little conservative

because we may end up with this crunch where all of a sudden we have to cut the latency in half or something.


because we may end up with this crunch where all of a sudden we have to cut the latency in half or something.


because we may end up with this crunch where all of a sudden we have to cut the latency in half or something.

Um. So , yeah , there are other things in the , um , algorithm that I didn't , uh , @ @ a lot yet ,


Um. So , yeah , there are other things in the , um , algorithm that I didn't , uh , @ @ a lot yet ,


Um. So , yeah , there are other things in the , um , algorithm that I didn't , uh , @ @ a lot yet ,

Sorry. A quick question just about the latency thing. If  if there 's another part of the system that causes a latency of a hundred milliseconds , is this an additive thing ? Or c or is yours hidden in that ?


Sorry. A quick question just about the latency thing. If  if there 's another part of the system that causes a latency of a hundred milliseconds , is this an additive thing ? Or c or is yours hidden in that ?


We can  OK. We can do something in parallel also , in some like  some cases like , if you wanted to do voice activity detection.


We can  OK. We can do something in parallel also , in some like  some cases like , if you wanted to do voice activity detection.

And we can do that in parallel with some other filtering you can do.


And we can do that in parallel with some other filtering you can do.

So you can make a decision on that voice activity detection and then you decide whether you want to filter or not.


So you can make a decision on that voice activity detection and then you decide whether you want to filter or not.

But by then you already have the sufficient samples to do the filtering.


But by then you already have the sufficient samples to do the filtering.

So  So , sometimes you can do it anyway.


But by then you already have the sufficient samples to do the filtering.

So  So , sometimes you can do it anyway.

I mean , couldn't , uh  I  Couldn't you just also  I mean , i if you know that the l the largest latency in the system is two hundred milliseconds , don't you  couldn't you just buffer up that number of frames and then everything uses that buffer ?


So  So , sometimes you can do it anyway.

I mean , couldn't , uh  I  Couldn't you just also  I mean , i if you know that the l the largest latency in the system is two hundred milliseconds , don't you  couldn't you just buffer up that number of frames and then everything uses that buffer ?


I mean , couldn't , uh  I  Couldn't you just also  I mean , i if you know that the l the largest latency in the system is two hundred milliseconds , don't you  couldn't you just buffer up that number of frames and then everything uses that buffer ?

Well , in fact , everything is sent over in buffers cuz of  isn't it the TCP buffer some  ?


Well , in fact , everything is sent over in buffers cuz of  isn't it the TCP buffer some  ?

You mean , the  the data , the super frame or something ?


You mean , the  the data , the super frame or something ?


Yeah , but that has a variable latency because the last frame doesn't have any latency


Yeah , but that has a variable latency because the last frame doesn't have any latency

and first frame has a twenty framed latency. So you can't r rely on that latency all the time.


and first frame has a twenty framed latency. So you can't r rely on that latency all the time.

Because  I mean the transmission over  over the air interface is like a buffer.


Because  I mean the transmission over  over the air interface is like a buffer.


So  But the only thing is that the first frame in that twenty - four frame buffer has a twenty - four frame latency. And the last frame doesn't have any latency.


So  But the only thing is that the first frame in that twenty - four frame buffer has a twenty - four frame latency. And the last frame doesn't have any latency.

Yeah , I wasn't thinking of that one in particular


Yeah , I wasn't thinking of that one in particular


Yeah , I wasn't thinking of that one in particular

but more of , you know , if  if there is some part of your system that has to buffer twenty frames , uh , can't the other parts of the system draw out of that buffer and therefore not add to the latency ?


Yeah , I wasn't thinking of that one in particular

but more of , you know , if  if there is some part of your system that has to buffer twenty frames , uh , can't the other parts of the system draw out of that buffer and therefore not add to the latency ?

Yeah. Yeah. And  and that 's sort of one of the  all of that sort of stuff is things that they 're debating in their standards committee.


but more of , you know , if  if there is some part of your system that has to buffer twenty frames , uh , can't the other parts of the system draw out of that buffer and therefore not add to the latency ?

Yeah. Yeah. And  and that 's sort of one of the  all of that sort of stuff is things that they 're debating in their standards committee.


but more of , you know , if  if there is some part of your system that has to buffer twenty frames , uh , can't the other parts of the system draw out of that buffer and therefore not add to the latency ?

Yeah. Yeah. And  and that 's sort of one of the  all of that sort of stuff is things that they 're debating in their standards committee.

Mm - hmm. Yeah. So , um , there is uh ,  these parameters that I still have to  to look at. Like , I played a little bit with this overestimation factor , uh , but I still have to  to look more at this , um , at the level of noise I add after. Uh , I know that adding noise helped , um , the system just using spectral subtraction without smoothing , but I don't know right now if it 's still important or not , and if the level I choose before is still the right one. Same thing for the shape of the  the noise. Maybe it would be better to add just white noise instead of speech shaped noise.


Yeah. Yeah. And  and that 's sort of one of the  all of that sort of stuff is things that they 're debating in their standards committee.

Mm - hmm. Yeah. So , um , there is uh ,  these parameters that I still have to  to look at. Like , I played a little bit with this overestimation factor , uh , but I still have to  to look more at this , um , at the level of noise I add after. Uh , I know that adding noise helped , um , the system just using spectral subtraction without smoothing , but I don't know right now if it 's still important or not , and if the level I choose before is still the right one. Same thing for the shape of the  the noise. Maybe it would be better to add just white noise instead of speech shaped noise.

That 'd be more like the JRASTA thing in a sense. Yeah.


Mm - hmm. Yeah. So , um , there is uh ,  these parameters that I still have to  to look at. Like , I played a little bit with this overestimation factor , uh , but I still have to  to look more at this , um , at the level of noise I add after. Uh , I know that adding noise helped , um , the system just using spectral subtraction without smoothing , but I don't know right now if it 's still important or not , and if the level I choose before is still the right one. Same thing for the shape of the  the noise. Maybe it would be better to add just white noise instead of speech shaped noise.

That 'd be more like the JRASTA thing in a sense. Yeah.

Mm - hmm. Um , yep. Uh , and another thing is to  Yeah , for this I just use as noise estimate the mean , uh , spectrum of the first twenty frames of each utterance. I don't remember for this experiment what did you use for these two stage


Mm - hmm. Yeah. So , um , there is uh ,  these parameters that I still have to  to look at. Like , I played a little bit with this overestimation factor , uh , but I still have to  to look more at this , um , at the level of noise I add after. Uh , I know that adding noise helped , um , the system just using spectral subtraction without smoothing , but I don't know right now if it 's still important or not , and if the level I choose before is still the right one. Same thing for the shape of the  the noise. Maybe it would be better to add just white noise instead of speech shaped noise.

That 'd be more like the JRASTA thing in a sense. Yeah.

Mm - hmm. Um , yep. Uh , and another thing is to  Yeah , for this I just use as noise estimate the mean , uh , spectrum of the first twenty frames of each utterance. I don't remember for this experiment what did you use for these two stage

I used ten  just ten frames. Yeah , because


That 'd be more like the JRASTA thing in a sense. Yeah.

Mm - hmm. Um , yep. Uh , and another thing is to  Yeah , for this I just use as noise estimate the mean , uh , spectrum of the first twenty frames of each utterance. I don't remember for this experiment what did you use for these two stage

I used ten  just ten frames. Yeah , because