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Multi-microphone speech dereverberation using expectation-maximization and kalman smoothing

: Schwartz, B.; Gannot, S.; Habets, Emanuël A.P.

Institute of Electrical and Electronics Engineers -IEEE-; European Association for Signal Processing -EURASIP-:
21st European Signal Processing Conference, EUSIPCO 2013. Proceedings. Vol.2 : 9-13 September 2013, Marrakech, Morocco
Piscataway, NJ: IEEE, 2013
ISBN: 978-1-4799-3687-8
ISBN: 9780992862602
European Signal Processing Conference (EUSIPCO) <21, 2013, Marrakech>
Fraunhofer IIS ()

Speech signals recorded in a room are commonly degraded by reverberation. In most cases, both the speech signal and the acoustic system of the room are unknown. In this paper, a multi-microphone algorithm that simultaneously estimates the acoustic system and the clean signal is proposed. An expectation-maximization (EM) scheme is employed to iteratively obtain the maximum likelihood (ML) estimates of the acoustic parameters. In the expectation step, the Kalman smoother is applied to extract the clean signal from the data utilizing the estimated parameters. In the maximization step, the parameters are updated according to the output of the Kalman smoother. Experimental results show a significant dereverberation capabilities of the proposed algorithm with only low speech distortion.