we got than uh my name is that was that in a little to and the to get that with the professor of S from a you know that's okay and our team of what what are you can would like to present to a variable step-size the proposed and make a a type of in which we call that yeah or a solution for echo cancellation uh is the outline as annotation we can see a what the production then uh as the basis of the proposed to eight of find projection followed you followed by a our proposed a and we and we simulation up company now a sense to the nice presentation yeah i a scale but mission if we if using production basically we we deal with the same echo cancellation problem we in fact is a uh system identification by with some challenge this challenging in uh i have a union our maybe is that a a long line or the equal parts so a lot no think what they are not if if you that and maybe the double talk situations see is much a challenging in there a right a this specific problem we the the more give a task for some special Y for example if we want to uh a faster again and then choose the family of affine projection oh which we know that the ah the more efficient from this point of us compare and that a man and we also if we we won to increase the robustness to double talk we can use the body of a step size we will see in the presentation of course in general the as a echo canceller and keep with a little of detector but yeah very useful you is yeah that we've algorithm it somehow oh was but seven to that and uh we have some of the hints let's say uh because course is the system we need to identify which are be a a i i i make is that the my is it so it would be useful to the use but to admit i adaptive of body i i don't by means this three each with result uh a the present a now just the oh but if for the each of a you of the problem uh this is the general okay separation patient what additional so and the a lot of course can be expressed in of a a ways for example from that use the point of view was the main but is to but couple but C know the near a signal in the all yeah that of course for all a point of from the application point of view we have to cancel this K well you not know what we have to like that if i is equal uh so this is the basic and a the application one now i is a uh talk uh and that the is the affine projection of what a a a a at may from the convergence point of view uh uh they but for very where especially we the in it signal inputs like speech uh yeah creation or the class a find projection of are you but yeah uh this is uh it true signal vector but this as the input signal signal my and most important this part i mean that you know the projection all that if you can see when P E close to one this is we used to a a lot and elements this is a a a a if you and and this is the step size for it that which we uh the following are those of its H now is that problem to me a fine projection of what you can you thing by extending the idea of pnlms in the same manner as we be form and lms the pnlms we can do for a yeah two well it's it yeah uh it up yes this that's a proper a marketing which in fact is that i i i not automatic speech a lost to a just just the step for each the in V but feel that way fish if you can see here let us denote by P of and is my it would be very computationally efficient to compute a in the class that way detection we can take advantage of the i are gonna a kind of of this matter and we can uh a process that it is yeah this um in this way uh so here B are not that sign set point vector is not the issue for a high number of do so we can come uh so uh P and market i simply multiplying is a a a a a a a vector of the proper five element by element with a vector or so yeah these not means element by element multiplication between these two but as we can see uh the classical for more sound but they simple color the content uh or what to make five recently recently we proposed a soul call member uh pop up to an the final projection of what which uh face into account not only the content factors bar that's that's study of these fact and most important than and uh besides the fact that used are going to achieve but that performance in at instant guess score that is that egg are i it is more computation efficient because if you take a look at C uh i we need to compute or is the first and N the first column and to use the out that form the previous it that nation and and do the same form for this map this can be a a you know that if you should manner of my computing or needs the first column and the first which is not the case here uh uh you you the it's uh each of which you more plates the uh a is that a on uh medical many complex that patient addition uh there a class a lot of loading and our proposed a of course uh yep and each becomes more smaller back and when we is the projection the and uh the next step but what ones which was to develop a of a step size fashion for this member in uh uh how you we we i here uh uh it's a eight and again it is the step size mean and is the we know always we have a complete think requirements when we have to choose the step size parameter because we have to compromise between combat and misadjustment well uh the bit rate in the double talk of who's that so the you know that two uh let's see compromise is used the and for out it yeah it would be nice to use a body a step size the and uh we start a lot development by rewriting and it will be this for a uh using uh a to use force the step side as as you can see from these two creations from this plot but it if we use uh is the same but for all and i and we can get a each step size are working which is you can here now if we take a look at a a a posteriori a vector or not you are here a the which are you bring yeah these these easy to uh what they no relation in these two that using these two and a a a of the seattle now we can see is a relation and if we we remember that to the basic i as you can find projection on reading once to chance and be a posteriori here the core we should slides seas assuming what was it man not well we will get a simple solution which means that all the step size should be about one unfortunately fortunately this holds already means absence of the nodes because is you remember from the for a in the echo cancellation problem we do not want to guess so yeah yeah are that if you don't by to recover is that near end signal from these and so we modify quantization and we it i uh this is the calculation that that then we take the might or might have a dictation and the we get the for as a a a a a step size of formal a like this which yeah we have the the elements of C L vector a since is this is a very of but it is easy to estimate is this five but unfortunately the problem but it mice the estimate of the power well of the near and uh as the problems comes from the fact that the near end signal is in fact a combination P the back noise and then you in speech you when is two pass are uncorrelated heat so we can write as this relation and even if we would basically to make this part and silence as for example it is difficult to have an expression for this uh i so you know that to solve this problem i i think that yeah that's that echo cancellation a configuration so uh we can express as you circulation in better of expectation one and that of our and now we will use a very strong assumption which is that is yeah now if you that has come back somehow list and uh get in this case if if you make a a a a a a is this the assumption of this relation can me really then axes of course as is this is a strong assumption because the i'm i'm for example is the beginning of the adaptation one but i station it may not hold by see E my the simulation that so uh oh performance a out of the fine right would uh uh most important if we take a look at these the relation to this estimate all this signal all this uh measure are available because we only use the signals from the that if you that that that that is that i know and it's all oh from this point of view it would be a dark a the solution so the answer to our problem is a state step size of a space step size which look like to use of course i i can be made in the same or say say that and the finally uh we do show some uh simulation results presented you know a network echo cancellation problem with a few that a lack of five and the the wave actions um the input will be you do white gaussian noise of speech and a the the performance measure a a a a a of the normalized means alignment or a according loss a and we will compare three at of buttons our proposed or the last one and it's a uh uh a fixed stepsize action uh uh as you can do these up here we do not mention anything about the alternate an eight five you at can be we can was any what what's an eight back what for i now uh you know uh i the experiment which is their i the elements of what in it's what what and fact factor was also presented by a monaural before uh in as our simulation they'd the bible i mean but is also the the trans and we compare uh is this struggling in with is that a have the step size non proposed at that should the action which is uh or they need to remove the for puts an eight might from these the ah in the first speed man we compare the variable step that should with a fixed at signs that's and was to a fighting for the step size as we can see uh as the body of the step size that's and combat almost as fast as the each step side that's start with a lot size but achieve a much lower miss i'm and close and that situation in this case we use the an input signal uh white gaussian noise then we uh are an snr all uh assuming that uh try patient the mean that relation and we compare our algorithm with a none of course but may action we can see that i'll hope of that is expected somehow a of problems a class L action in that of well convergence it or that just this alignment and finally uh uh you know that what looks and i not uh we compare at all the three hour and uh we see that for this point of view from that was to double a point of our our that it's much more what was as compare yeah at least is uh a non of a step size yeah we use a a simple get about that that a vector just two and the adaptation double talk i in it is a a a a few conclusion so we propose this out that point text of for echo cancellation first we can take advantage from a its computation of complex T which is low as from that is a class second we think that is important that the value of a step side for somehow somehow not about made this means that we do not use any additional power uh i'm sure one by one and we also note this is that uh these more what used to uh near end signal evaluation and most important to double thank you very much earlier for that well we do you have some time for i if anybody like using again you presentation presentation just them curious about two the step size it becomes negative when the values of the design a value is a lot to than that of the there yeah so in this problem okay this is a very nice for because it we ski but it to meet some practical issues yeah it is a recommended fast to at a small positive constant yeah just to avoid you bytes at most important we should they is the absolute but this a three star is then the but but what i too much but if inking your simulations used in order a projection order for for for yes and is the method sense to the project no no we just to choose this value it's a for complex but we try was my projection of you have and in your a personal experience what projection order do you find is really the the right and it to be working in for for uh i think that uh to three we well we should not to take a larger than eight that but also because of the complexity issue you by that you for this these some uh that's a diffusion map for completely because in a fine as the fine projection of what you so main problem is how to invest the out i i have a project the higher projection of but we been blind or if you a quotation my recently the as far as i no was that are some more efficient manner for example you you all because the models i or the might be end variation of by well also a which she uh if you lead that for this operations so we can even go higher a project and other question i think i have the opportunity to last one um a more general question concerning the difference between acoustic echo cancellation and network and could you clarify which i think you first results at least as in the network sure yes a the was the so is the star a point of two and eight type of that if you that oh for a network that for consideration and respect to that form but that's send send you because of this i of the because i buy it can fall you by room acoustic a more cancellation if we properly choose the proposed to eight five this was that he's and defines the chose of the I the nlms out which is somehow of a host to this ask part think the of course the we can use for example a low as far as and uh as i we proposed a few a one year ago goes as the sparseness control coding which can a beep on uh well efficient as compared to ipnlms mess if we know as this from miss what it's to make some the smell so we is important to choose the proposed well that's great thank you very much and