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Talk Abstract
Seminar on Industrial Problems
November 2, 2001

A New Class of Speech Signal Stochastic Models and Applications to Blind Source Separation Problems

Radu Victor Balan
Siemens AG
radu.balan@scr.siemens.com

570 Vincent Hall
10:10 am

In this talk I'm going to present a new stochastic modelling of speech that takes into account the sparseness of the speech signal time-frequency representation. More specifically, we assume the time-frequency coefficients can be factorized as a product of a Bernoulli (0/1) discrete random variable and a continuous (often Gauss or Laplace) r.v. We approach the Blind Source Separation (BSS) problem having this model in mind. The (acoustic) BSS problem assumes a number of voice signals are recorded simultaneously using a number (possibly different) of microphones. The problem is to separate the speech signals, based on the microphone recording signals only (hence "blindly"). We consider the case of two microphones and a larger-than-two number of signals. In this analysis, we develop several mixing parameters estimators, as well as signal separation procedures. This is a joint work at Siemens with J. Rosca and S. Rickard.

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Mathematics in Geosciences, September 2001 - June 2002

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