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Re: Blind signal separation:"Cocktail party problem&
problem looks nice.. I also once studied Independent component analysis and Multiple De-correlation technique for Blind Source separation and i also discussed this topic with one Post Doc from Holland who worked nice on Array signal Processing and Blind Source Separation. So i am intedending to opt. this topic for my phd and that's why i am starting initial work on this topic. It would be great if you share the m files and the text related to your problem. I will read this and discuss with my advisor so then i can further discuss with you if you like.
Re: Blind signal separation:"Cocktail party problem&
We present novel techniques for blind source separation of anechoic under-determined mixtures of signals. The algorithm is based on a two stage algorithm whereby in the first stage we estimate the mixing parameters and in the second, we use those parameters to perform separation. We base the second stage on solving a constrained optimization problem, where the constraint comes from sparsity assumptions of the sources in the Time-Frequency domain. The method seems to work well for the instantaneous case as well and for some echoic scenarios
Re: Blind signal separation:"Cocktail party problem&
That sounds awesome. Would you mind sharing some of the work that you have done with us in here. Like some paper that u might have written, as well as some matlab files and descriptions...
Re: Blind signal separation:"Cocktail party problem&
hi
I am working on ICA algo for solving cocktail party prob
I need to code it on TI proc 2812
Can somebody plz give me simple matlab codes tat can be runned on the proc?
Hello, i am working currently on algorithms for solving "cocktail party problem". I am interested if anyone works on this topic. I would like to exchange some knowledge about this topic.
Re: Blind signal separation:"Cocktail party problem&
Hello everybody. I am student from Japan. I am working on the simplest version of cocktail party problem and I am a beginner in Matlab. I don't know much about Matlab and how to implement ICA algorithms into Matlab. Firstly, I need to buy Matlab software and practice how to use it but I don't know which softwares (version of Matlab, package, toolbox...) I need to deal with cocktail party problem.
Could anyone help me? Thanks so much.
I have just started studying DSP so kindly tell me which topics will i need to cover before it start working on BSS?
also i am a newbie to matlab ... so also mention what features of matlab i need to learn first?
can i do it without matlab??
"Cocktal party problem" can be described as a situation when we have 2 (or more) speakers talking simultaneously. Each speaker is a source and we record a mixture of the sources using 2 microphones (sensors). Each sensor contains a mixture of voices of 2 speakers. It`s a 2x2 MIMO system. The task is to recover the individual sources(voices) having only the record of their mixtures (the mixing process of sources is unknown).
By this time i wrote an algorithm in matlab which solves this problem.The recover(demixing) process is done with natural gradient adaptation algorithm.
When i tested the performance of demixing algorithm i took 2 wav files,one with the voice of my brother and second with my own voice and mixed them together. The mixing was done with a mixing 2x2 matrix by multiplying matrix by source vectors to give 2 vectors of mixed speech ( 2 sensors). This system is invertible so if we have the observations of signals (records from sensors) then if the mixing matrix is known, original sources can be found by multiplying the inverse mixing matrix by sensor vectors. Of course in blind signal separation tasks we assume that the mixing process is unknown (we dont know the values of the mixing matrix) and we want to recover original sources from their mixtures.So it can be said that problem will be solved when the demixing matrix will be found.
I hope that the whole problem and solution is clear. In my research i want to compare different kinds of demixing algorithms. The next algorithm i want to concentrate on is a demixing algorithm using genetic algorithms beacuse they perform well in finding optimal solutions.
I also plan to model some acoustic path( like a room impulse response) when mixing sources: adding echo effect which will affect that mixture will be with an echo effect and then recover orginal voices without echo.
If someone is interested i can put my m-files and also some material coevering this topic in here.
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