i but thank you and the work has been carried out uh and good morning and the work has been carried out uh in the department of electrical and computer engineering at the university of buttons in greece by a at yeah your run D professor them would open as and my sense and uh the work is on the binaural extension of single-channel channel spectral subtraction reverberation i'm reverberation has been a challenging research is you for at least forty kate and now the verb techniques are applied either there are standard as standalone process in order to enhance the reverberant signals quality or even uh increase this reverberant speech intelligibility or or as preprocessing steps before other several signal processing algorithms and applications in know the to increase their performance and one one is developing uh binaural dereverberation algorithms uh it should also take into account some constraint that are imposed from the binaural aspect of the or a system so as we all know um when the sound that i've in the left and that a right E channel here of the listener it does as with a relative delay and the relative late uh level different and these so-called binaural cues are important for the localization of sound the sound space and this should definitely be preserved from the binaural signal processing in general or and more specifically for the binaural from the binaural dereverberation algorithms on the other hand binaural reverberation has very appealing applications it can be applied in hearing aids in binaural telephony in hands-free devices in most of the code a telecommunications so uh recently we have proposed in our lab uh some single channel dereverberation algorithms we have proposed a framework for improving single-channel channel existing spectral subtraction dereverberation algorithms we have also uh presented a novel method um of high computational complexity that gives uh perceptual sick need perceptually good results which is based on perceptual reverberation modeling and also a fast uh semi-blind reverberation with that's which is based on the hand club recording which targets speech application so the state for what step for us was to extent uh sets a technique and the binaural context and and the most of those uh thing to do was to extend uh the spectral subtraction dereverberation which is uh techniques of low computational complexity when compared to sophisticated and what that the remote pro so the specific gains of this work uh is to propose a single frame frame for the extension of single-channel channel spectral subtraction dereverberation algorithms two uh into use and efficient way to prevent of estimation errors and also to evaluate the proposed framework in several state-of-the-art spectral subtraction dereverberation technique um expect that subtraction was originally proposed for D knows in application but recently it has been applied for the suppression of late reverberation we all know in room acoustics that after the direct sound the L reflections are i've these are discrete echoes that come from the close surface and produce spectral uh degradation that is perceived as colouration in the diffuse feed the late reverberation arrives which has a the gang noise or a like characteristics and he's perceived as the well-known signal a reverberant tails so in the late reverberation suppression some context spectral subtraction uh gives the any coke estimation by simply um subtracting from the reverberant signal and and then uh and uh estimation of late variation and mostly liberation separation methods that work can this way how to uh estimate exactly these late reverberation spectrum more power spectrum depending on the method and let's look some state-of-the-art methods yeah the methods proposed by where wine gone for we can cut out come from a one will refer to them as W W an S K A i i taking someone assumptions on the reverberant signals that these six while the well known uh reverberation technique from bar to and then be uh uh from oh no we refer to this as a B is um a concern assumption on reverberation characteristic keep in mind that we can easily express the subtraction um a principle as again multiplication uh in the frequency domain by deriving the appropriate gain so the a straightforward approach would be to implement separately in the binaural context uh independently this uh late reverberation suppression technique for the left and the right channel but it has been proved that the lateral signal processing will destroy this binaural cues and uh it will make the localization in the produced signal uh be distorted so and in the bibliography be i hitting a can team has proposed uh spectral subtraction extension which is based on the delay and sum beamformer uh by beamforming by actually at thing at the left and the right the channels and synchronizing then um it produces a reference signal it then makes the late reverberation estimation and the signal and then it apply spectral subtraction independently uh in the left and the right yeah and so the binaural cues are present in these work uh i will make an extra samson that the relative delay between uh that to um E S i actually depends on the weight of the human head and it can be assumed that it would be uh smaller than the typical analysis windows so we for this work uh we meet the delay and sum beamformer state and we propose a binaural extension which is based on a single channel uh spectral subtraction dereverberation on based and lateral again of station a see the signal flow of the proposed approach uh separately from the web left and the right a rubber and frames with the two different estimations and uh know the to derive the bi lateral games then these gains are combined with a chosen a again of the patient seen in order to to give us the binaural game then again my to the regularization seem that prevents from of very estimation roles that we introduce here is applied in order to give us a constraint binaural again which is separately independently applied on the left and the right frame the gain adaptation for the gain adaptation in this work was chosen the or to use uh started is by taking the marks again in it's frequency being uh we had seemed more it's operation and fewer processing artifacts by taking the average gain would be the compromise between the reverberation reduction and the processing folk while the minimum gain give significance of print so oppression but it can be easily introduce artifacts so the selection of the gain of the patients one was made according to the application scenario you know there these blind method as are uh use and introducing uh signal artifacts and to not to to prevent from such of estimation not different um we have uh probe proposed here we introduce here again a market to the regularization step which is implemented uh in the low signal to reverberation or should detector the assumption here is that um musical noise or yeah other of estimation that the facts will a um we are more probably to uh be present in low signal to reverberation racial frames and this these and didn't regularization sing uh depends on a regularization application of to see that and on a regularization ratio are these are user defined parameters that can be a just in order to um control the suppression rate so this that um while properly uh just adjusting these parameters can compensate for estimation error and prevent musical noise further explain uh the use of these parameters these are typical spectral gain functions and now by keeping seat that to zero point two and are equal to uh are equal for an a equal or are equal eight we can see how the gain functions saying and but keeping think to uh uh are constant we can change the two zero point four and zero point sick so we from here we can see that a that can be used for the but note um control of the separation range why of the parameter R can be used for fine tuning the method uh let's present some results uh these results um are uh um made with um measure at um i impulse responses these uh a specific uh a is since a given from the i can that the base yeah that the base in the stairway away for uh with a reverberation time of zero point seven approximately note the to evaluate the results uh we used to metrics the signal to reverberation or a should difference when compared to the reverberation to the reverberant signal so pos difference is be note that the um more significant reduction and also um medic the pesq Q uh difference when comparing to the reverberant signal which relates more to the perceptual uh quality of the final result uh we implement uh this three by a binaural gain adaptation the patient started is as well as a delay and sum beamformer or in three state of the art a spectral subtraction dereverberation algorithms V L B W W gone of gay and as we can see uh all of the then any can me significantly reduce reverberation as we expected the mean gain adaptation seem we'd uses more reverberation while the marks gain less and when seeing the where P Q difference which makes more sense in a from a perceptual point of view we can see that the W W method with the mean game technique uh gives slightly but the results these results are taken in the at the uh from the all the would that the base and these cafeteria has uh high reverberation time of one point three seconds and um ooh as we can see that is the reverberation reduction here is um smaller and it seems that such techniques in the sets reverberant conditions uh and enhance the final signals but on the other hand uh the enhancement is less than the previous case again uh the W W to can uh technique i had achieved uh but the results in terms of um S R are and press and uh the best results were uh were observed for the average gain adaptation seen so we not there to presents some further evaluation we conducted um subjective evaluation test this test was based on the I T U B eight thirty five and recommendation and seventeen test subjects participated in the test uh we made by a look test not the to get to test the um two choose the best of the station and seem for the set it's techniques so for the L B and W W technique the average gain adaptation was chosen while for the S T A an meaning i the M meaning gain technique was chosen and the test subjects were asked two or rate the speech not real nice they reverberation intrusive an S and the overall quality of this speech signals um for a in a most K from zero to five so from these results we can see that uh the test subjects rate the dereverberated signal i net less natural in all cases however and we notice a significant reverberation reduction and also at least the L B and W W techniques preserve the signal quality the overall signal while and a for gently we need um headphones phones know the to diffuse some them one but if anyone is interested uh that then was out of a are available in the web of our group um B website is also in the paper uh is written in the paper so to sum up and we have introduced a framework for five binaural spectral subtraction dereverberation which is based on bi lateral gain adaptation the gain map and the regularization seeing that we introduced can read use the over estimation errors and produce some uh uh and um preserve from some uh uh the gradations uh processing the gradations the selection of the adaptation seem and the D M parameters uh can be made according to the application scenario and there is also significant reverberation reduction uh while the overall speech quality and the binaural cues are can be present how there we noticed some loss of speech naturalness so for the for us this indicates the need for native binaural mode it's models that take into account the binaural properties of the to the system and this is on what where working right now thank you very much okay um we have time for a few questions you that questions can you just use the microphone over there any questions from the audience and questions okay maybe i just start okay a how do you oh man on the uh uh the accuracy of this been all real oh and uh cues preservation uh this is a big problem because actually we the a perceptual test that can and exactly and um read the of on the on these these need to really control the um and um environment and so it was really difficult to do so it's that's actually uh i think that i i'm not aware of uh and it test for reverberation a graph that and and um uh exactly uh predict the these uh binaural cues preservation um this is the for the for further investigation so you have not done any subject you test on this on these snow the questions you know we best you really uh another question is how do you did the mean this power meters you know G R G M R or uh these parameters actually depend on the frame length and on the reverberation time on how to store to this you not signal and we give some uh range for the parameters in the paper so uh actually the they are different frequencies range for it's sampling frequency needs frame length that's the user can that know the to take the optimal results so for your experiment or for simulations are a bit sorry use no we made by look test to tune the parameters for these a rules what different environments yes any questions so you've not last thanks the speakers again