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

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...

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Bibliographic Details
Published in2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers pp. 343 - 345
Main Authors Bensaid, Siouar, Schutz, Antony, Slock, Dirk
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
Blind sources extraction
EM Algorithm
Gaussian processes
Kalman filters
Microphones
mono-microphone analysis
Periodic structures
Predictive models
short+long term prediction
Source separation
Speech enhancement
Speech processing
Technological innovation
Telecommunications
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Summary: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.
ISBN:1424458250
9781424458257
ISSN:1058-6393
2576-2303
DOI:10.1109/ACSSC.2009.5470079