0:00:15 yeah thank you um and let's the some audience left for the last talk of today day and a the it is uh a of different to the talks before um for for getting line of mike talk we can just this is uh i to the book were that's uh uh i was taught to given that very short introduction seduction what lights also plays say it what's the problem can load it's case may need to permutation and the greedy and how i'm solving in using as sparsity basically criteria so um and the you a case for like some separation is when you have a cocktail party problem um we have some sources uh at this point i say we have speech sources to a people talking and he would like to get sing the components of that uh but a what what you get a some recordings which are just make chance these send single components and um in this case but i'm looking here uh uh we have the better problem off to the uh mixture of being convolutive one as we have to of speech we have reflections and so and so on so uh the problem becomes more complicated and the mathematical formulation for this um we have some source some extent and matrix at least for the instantaneous then use case i gets of measurements and what we want to do is to a estimate might matrix uh separating matrix so we get again to uh i'll in a signals uh for this we had like the ica so nothing you at this point uh what we have to um take into account uh we never now the although of the sources and you never know which energy the sauces have um in my work i used to done not feature a of the natural gradient uh as i think if you but you know oh for speech signals we need uh as always we need some a a probability dispersion functions for speech what when considering here we can safely assume uh we have using a class industry so as i you said you have uh not to simply case we have to convolutive mixture we have a in this case you you different delays you have to reflections and so on so we model this using uh a convolution and uh four we a situations we have some known that to us two thousand four thousand taps or whatever um estimating these filters directly in time domain is hot possibly but very hard so the you wouldn't way is to go to the a time-frequency domain using the short fourier transform and now what we have is just again uh what implication in each frequency bin so uh we can just use the uh up to a to are you shown in each frequency bin independently which is again uh not a problem but no we have the problem of uh the different and rotation patients and and scaling things uh and the previous example can do in you think about that in this case we have to correct um the scaling uh there some standard was you have to solve it uh the typical the case is the minimum distance or often principle uh which we multiply the i'm next matrix by yeah and the with to tight on you down at them and uh what this and that's that we uh X and and scaling done by the mixing system you do not know which was but at least we do not at new distortion just point um some new method uh presented and last time uh a filter shorting filter shaping but for these masks that you need well um solve the permutation problem first uh well it's as uh you can so it didn't each frequency bin independent so we were talking about the permutation problem what what is so how can be uh well uh scrap in this case we have to short time the some space two spectrograms for time free transform of two signals where just when you exactly know these spots a swell between the do use two uh spectrograms when you are we start these signals back to time domain of course both signals appear in boston channels so again you didn't uh separate and so you have to correct for use permutation and these can be uh and every frequency band different and usually comes quite complicated uh usually the two main approaches uh the a lot of paper as in and of friends concentrate on on direct T V two patents and directions of arrival uh the idea is when you have to or mixing matrix as uh we can just uh calculate to directions with a some come from and assume uh that one direction is one source this works good a strong we have low reverberation but i reverberation uh you can't um um pinpoint point a the sauce to thing the direction in all frequencies together uh in this case here i i used the statistics of the separated signals um one trivial simple case is uh you just look such a a line in the neighbouring nine in this say i they have to look to same so they here they are highly correlated um yeah this is true does this at least for when when you are looking for a very near bring bent so we have here to a wreck neighbouring bins and blue and green and yeah okay yeah highly correlated if you just go a few bins away yeah i i wouldn't say these been covered so the correlation method is not so to robust but uh they have been extensions to make it uh a lot more robust oh okay so yeah at these um the correlation coefficients uh take the um and then low calculate the correlation and decide the pen what station depending on all four possible permutations take and then and uh using is uh uh are you can just use a this this way as a already said this isn't very robust you have to make it a because of the yeah when comparing more distant bins a you just got wrong uh and then so um and you years ago uh uh just been proposed it is the other so think she you as proposed here but you don't compare single bins uh yeah but how blocks of bins so that the S luck like this you compare it's a first stage you compare one been but another zero you one and calculate a couple correlation can created in and you get you permutation and take the next to bins and so and so on so in this case you have neighbouring bands and you can assume okay to assumption to five related bins it's met in the next step you take these to correctly calculated bins take to two and calculate now uh these four collation so actually what you get F here for coefficients and we have to decide which one to take to you site which can eight uh which permutation do we take to big as one to mean to always one or whatever four but not a problem here you go to already sixteen and the next yeah we get a sixty four and so on so it becomes even harder um a simple example for this um when we just plot for the the situation but for a frequency bins um the coefficients yeah um for all frequency bins so and the first page you would just take the correlation it C coefficients directly uh on the first of their i don't know uh and a uh okay when you look at this it's looks like just go to uh well it just one here and here hardly so when you going next up to next steps so that's say you compare the block five from that to eight hundred to the block a time that to one thousand we on that or whatever you compare all the coefficients well which are and a square so we have a lot of coefficients which are correctly and a lot of coefficients with or not can and and so on in this case here K as we work here are not but in the next steps you compare these coefficients a K just me still worked as might a stable but this case here if a lot one computations which is a lot of indicators of our limitations which in a right and one conditions so usually the dyadic sorting scheme is that are but still phase but so and signal um no i want to um a present if you approach uh the first uh observation i i and you can make it when you're just take speech signals speech signals as past and um a mixture of two signals which are in a independent this last and a you can extend this even if the signals are on a signal as long as the independent to mixture is less spots and just is exactly what we have a a permutation problem we have to bound a signals and one to look which permutation do we have so the wrong permutation will be uh a past a a you have he an example of this uh just to plain speech signal but nothing hadn't yeah and in this case i just most to hi are that's uh uh of of the signal so that hi up half of the signal to the other so we have to mutation and the lower level of of the the R T K that sorting scheme and when we compare these we have here a lot of you was or more zeros and when you look here we have clearly a signal which is less spots and uh this is exactly what we need to uh from late the a new criterion you want to signal to be S sparse as possible uh the measurement of sparsity um for this is an hour of uh to take to some new method of the lp norm uh in my case cases a usually it takes something like zero point one for for P but it's not that and part you can vary um okay so uh i there is no S with the correlation coefficient we take our signal calculate no not the correlation between two signals but the sparsity of a sum of two signal and take again the four coefficients every every one against each other and you get one um yeah coefficients coefficient which can decide which permutation the point think about this snow we don't take the coefficients in the time-frequency domain but D transform is point process coefficients to uh time domain signal where we can apply it it you know uh using this even if we take that's a hundred frequency bins from K to S still again P that the calm just one and coefficient for the whole sorting she so when we now know do the the are or thing so we have again here and frequency just one thing the frequency band transform to the time domain he again one E applied to you know is and here again and at this point that is uh different no we transform to frequency bins the time domain and calculate again one comes and so and so and so so it's this point you don't know have to problem of which coefficients of this that's a thousands or or whatever do you do you takes on you uh but you have always just one coefficient and due to the different criterion uh a a it's it's much more robust mostly um i have done some simulations um so first set uh uh data set this does a for the set up use go T um so on so about last they can set from five years ago so um we have a separate this this state set that uh is the lot uh somehow it's a reverberant recordings some some speech but to relation is quite whole you can when you hear of to is that has that you can see yeah it's government art derivations like this this case the direction of of uh an approach it's very good um it it works because of the low vibration the proposed method it not as good almost but uh when you're local closely Y is performing not that good it's because it's a very low stage where we compare just one thing and frequency bin i yeah uh happened some limitations to and correct and uh so perhaps uh should it this to get so that a bit more if uh is assumption of sparsity and solves a a one pass cygnus is of this is correct and um but when you going to a a set which uh a that the cartons that high reverberation uh all over you got less uh suppression performance the do approach is because it with to set up you don to have the uh the signal coming from one direction because of the reverberation but the new approach we all again get almost the performance of the non right algorithm uh because this case um you don't matter which direction to signal comes as long as we i able to separate it in every frequency bin and um um so it's not always matching the non by case but it's more robust compared to the signal it's of the dot pro so to conclude um the converted by source separation can be soft and the sorry time-frequency domain a you have to solve the scaling and permutation and no we presented a new algorithm based and sparsity in the time domain not as user a and a dating time domain and with tire of variation we have usually better separation performance and there direction five uh so yeah let's a hard a set up it's like seven and a half set and for this i used five seconds i i saying if an a signal uh enough signal to make i C in each frequency band then there would be enough signal to make you you know