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Mono-microphone blind audio source separation using EM-Kalman filters and short+long term ar modeling

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3 Author(s)
Siouar Bensaïd ; Eurecom Institute, 2229 route des Crêtes, B.P. 193, 06904 Sophia Antipolis Cedex, FRANCE ; Antony Schutz ; Dirk Slock

Blind sources separation (BSS) arises in a variety of fields in speech processing such as speech enhancement, speakers diarization and identification. Generally, methods for BSS consider several observations of the same recording. Single microphone analysis is the worst underdetermined case, but, it's also the more realistic one. In our approach, the autoregressive structure (short term prediction) and the periodic signature (long term prediction) of voiced speech signal are jointly modeled. The filters parameters are extracted using a combined version of the EM-Algorithm with the Rauch-Tung-Striebel optimal smoother while the fixed-lag Kalman smoother algorithm is used for the initialization.

Published in:

2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers

Date of Conference:

1-4 Nov. 2009