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MMSE-based source extraction using position-based posterior probabilities

: Taseska, Maja; Habets, Emanuël A.P.


IEEE Signal Processing Society; Institute of Electrical and Electronics Engineers -IEEE-:
IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2013. Proceedings. Vol.1 : Vancouver, British Columbia, Canada, 26 - 31 May 2013
Piscataway, NJ: IEEE, 2013
ISBN: 978-1-4799-0357-3
ISBN: 978-1-4799-0356-6
International Conference on Acoustics, Speech, and Signal Processing (ICASSP) <38, 2013, Vancouver>
Fraunhofer IIS ()

A scenario with multiple talkers and additive background noise is considered, where some talkers are active simultaneously and the activity of the talkers changes with time. We propose an MMSEbased method to blindly extract any talker using bin-wise position estimates obtained from distributed microphone arrays. In order to distinguish between different talkers, the position estimates are clustered using the expectation maximization algorithm. The resulting posterior probabilities allow to estimate the PSD matrices of the talkers and compute an MMSE-optimal linear filter for extracting each talker. We evaluate the performance of the proposed method in terms of noise and interference reduction and distortion of the desired speech signal at the output of a multichannel Wiener filter.