Abstract
Blind Source 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 is also the more realistic one. In this article, the autoregressive structure (short term prediction) and the periodic signature (long term prediction) of voiced speech signal are modeled and a linear state space model with unknown parameters is derived. The Expectation Maximization (EM) algorithm is used to estimate these unknown parameters and therefore help source separation.
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Bensaid, S., Schutz, A., Slock, D.T.M. (2010). Single Microphone Blind Audio Source Separation Using EM-Kalman Filter and Short+Long Term AR Modeling. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15995-4_14
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DOI: https://doi.org/10.1007/978-3-642-15995-4_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15994-7
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