and and there a one um of rough urgent phones at university from calm five for a or two presents a joint one with my C for vices it can uh the topic is a analysis synthesis based speech enhancement we is improved spectral envelope estimation by tracking speech time then so uh first less have a look at our line for for my presentation first at the very beginning i where we introduce some uh but runs uh as a spectral you all some a a effect by noise corruption conventional filtering ring and now introduce a model based based speech enhancement uh which is a previous proposed by us and uh i work then introduce a speech tracking speech dynamics tracking scheme that is used in conjunction with the model based speech enhancement and uh uh performance evaluation a cushion you so uh let's to have a a first have a look the effect of noise corruption from as a true perspective yeah use a white noise for example we can observe that the harmonic structure of speech as the C V a lead image and the the the special name a lot is now which are out in a lot of a spectral distortion and the we are the is some uh mention no statistical model based as speech and and has meant to and the the the the upper figure shows the classical oh lot special an impact you though and the from the job times spent on we can see that the lower portion of the special and have been restored but the overall noise level can not be um where was suppressed so as a result there will be many is music tones and the wrist reese residual noise is in the clean a a process the speech and the the lower or figure shows that uh optimum own modify the log spectrum them to do you and the these not the generally have a very good at it a cat ability of office noise suppression and but however the form men and of the harmonics but structures um for that just talk so um that a of of us also um often you are better pass goal but the and the wild the uh lower you go gives a better segment low snr school so there is always a tradeoff the two in the noise suppression and the the harmonic distortion we can say the naturalness of speech so uh can be also observed from the spend joe special special model that no voice will first a C V are they just thought of this special name model and the can measure no statistical method what the partial you're restore the the spectrum am all but a partial of for that just the spectrum so this what potentially a con for some comment and features as such as music tones and the low intelligibility problems in um speech enhancement so you in our our previous work we have proposed um analysis synthesis this approach based on the how most model so that basic idea is to it's track a close to Q from noisy spatial and the down we reconstruct the noise uh the type a speech by re is this using these speech only information so you can see had from yeah you have a speech information so you can have the track the location of the harmonics you have a actual again so you can have that all are average spectral and at level and you have the special envelope you can have the track uh many to respect so why use this what we choose this approach to uh to do speech enhancement first this model was cape escape bow to generate clean harmonics and that only speech related information is size so i background noise is out to me be removed and the this this model also and retrieved some then each harmonic structure and that as moves spectrum would hope so so no isolates spectrum peaks and the hands no meats we from one problem and also this mortal allows at independent adjustment of different more apparent so it in a thing and both ask to for was or N has the spent M role and using this framework so by you think this now thought at we can you uh we can suffer from the noise suppression and the the harmonic distortion trade so from some uh of of our previous work um after we uh applying some for clean procedures using conventional method that we can apply the pitch uh frequency domain pitch searching and that that a a spectral again estimation some um really preliminary result shows that that P H and the spectral gain estimation already already give very good performance by a a a a a pine on the perfect in the spectrum however the spectrum envelope estimation is someone and ad so uh we can see yeah for some are really made a result shows that the the past goal for uh uh there a do you want noise would give a already one point five and the some um but can measure an approach what a run one point nine and the our previous approach take D vol you with this pretty clean and can give a uh also a a a point to uh improvement however it's we replace the M brought with a to clean rule this that it can achieve three point one seven so it is huge huge got here so we we would expect some improvement in past call if we can further proof spectrum them so that problem can be state as a so for each frame use frames of noisy observation uh we want to find a mapping between the noise and train spectral envelopes and of full can set sec two frames we want to find that temporary tried to juries of clean special neville oh uh i in other words we want to estimate clean speech and by looking for long term speech you pollution so by as you me uh over us certain pure at time uh a the S U yeah relationship between the consecutive clean spectrum blobs and uh a the in relationship between the noise and clean special on them we can use that lenient an just the model to more though this uh state chucking so the the feature we used here is uh a a line spectrum frequency of lpc coefficients and uh and the for each uh pure see each cu result all observations so we have well as C a series of lpc coefficients so a given a comments system few uh part meters we can run it um um i and the yeah oh ten clean L quite vision so the next proper or what you how to to ten uh a a common system permit us for each the year is all which so the idea is that for each block of noisy observations we find a a a a we we use the for each and the culpable that but also the class did parallel i lpc coefficients and the to through some uh optimize region quite your we can all to and the corresponding i meant them permit so in that all fine chaining just we have all noisy and noisy and clean uh L C coefficients and the we use those spread B Q to um sure a to and uh global and trace in the sense that blocks with similar be sure a a group into the same class us by saying a similar we need to do define a distortion measure here it could be is uh something vol measure as a a you could in or you can use the some contract manager as as a uh as a uh modified i S measure and the you also a to define i'm feature for each prop of all persuasions you can use that average just special or you can use all of theories of vectors so it it what it actually be a a matrix quantisation quantization you this case and the a for each cluster we have both noisy and clean up so uh observation a noisy and clean features so we can minimize the total neck a like cool function for each cluster and we will oh to and the design the comment system them permit in this case so you know i like adaptation up they just we we we also have a a noisy observations for a block and the we use the say at this this that's measure to find the cop and trees and that has the corresponding comments just the parameters and the were run their common are we is uh as that's of permit us and we will get the design better on them so you can so from the spectral round yeah that the tracking actually gives very good uh performance also have from uh three D view that the a noisy the noise the envelope trying to juries a quite mad and the flat rate and the some get this conventional mention P of a read what the re risk oh some harmonics but a resulting some use one problem when but that the this most tracking subject use here which give various moves and uh uh a and accurate to to re so it is it can also be observe from this figure that the for is that a spend it the phone then with expend as compared to the conventional map so the tracking gives very close to the original spectral envelope try to the right so uh there's still time men do spectrum and that harmonic structures uh also to and the from the fine find or size speech we can see that uh no smell or um it and no use homes and the harmonic structures i retrieved and the actually we can achieve a run to phone for one pass school for speaker dependent trendy and the the uh the noise we use it is a from there are to ten db or uh uh using a white noise car noise and uh uh a a be noise so a speaker dependent and this be in the pen and testing is used and it finally uh i can group the presentation yeah in this paper presentation we uh we've block at the effect of noise corporation an cry option and the conventional speech enhancement it's got as been just got and not and not it seems this approach is present and and speech dynamic tracking important that incorporate you change in the common ring as proposed and they prove a special name estimation is illustrated objective to in terms but spectral distortion and passed call i show so at so for my edition i think you yeah that so much yeah this the first question you have be audio samples are and then have you can up to uh bring with me so you're yeah yeah yeah i was then some good it yeah i you can sort or could you come on C P U cost to issues oh um actually i you use that a a a for training it will be time consuming a will you out but you can can show that all the in your protection in of the uh a a a i thought of size so that that's always a tradeoff okay fine tune is full set able let that's quite lot yeah a next question please from your presentation uh i realise that the on is by send is according to clean signal be or upper bound right yeah the on nice is sentences results according to the given to clean and but of the be you upper bound of the optimal case would be the time to lead to show that clean yeah why is so my question is is you had in is said the on effect of D they a noisy phase information that you using your sentences so um what will be the exact a of the uh noisy envelope and the noisy and fate information that you are using a would is in this case in this work we just use the many do spectral we have not uh look at the face ms actually uh in college it has enhancement uh a face not selling four improved by um research could a fact the intelligibility so uh uh maybe in free for sure works we will come but for your information the were some papers also talking about the importance of phase information in in the T a made as which are work you know yeah you this is a have a some i mean that a gap between the upper bound and the proposed method of your can also be because a that's a noise it face so this check this scheme is uh this lee what well for voiced speech so for um voiced speech we we can just use some pretty clean the data so this would be something weird asian for for form a gap between the optimal proposed but i would be interested to know what you need to a voice activity detector all actually we have tried to use the void you D to trend voiced and i'm voice for different that but there is out there that we can sure that trend that one class to pull or data it's you better for for the whole tracking yeah you synthesis model is very yeah adequate for it's a sinusoidal model for approach using yeah voiced sounds how do you put use the unvoiced sounds so the unvoiced voiced sound it is basically uh no P and uh we just used that um uh a a a a boy time the women port two seems size to have a gain information and P information and just commit time domain and yeah i you are they have for of the questions that is not the case thank you once more