LVA ICA 2010, 9th International Conference on Latent Variable Analysis and Signal Separation, September 27-30, Saint-Malo, France / Also published in LNCS, 2010, Vol 6365/2010
Blind Source Separation (BSS) arises in a variety of elds in
speech processing such as speech enhancement, speakers diarization and
identi cation. Generally, methods for BSS consider several observations
of the same recording. Single microphone analysis is the worst underde-
termined case, but, it is also the more realistic one. In this article, the
autoregressive structure (short term prediction) and the periodic signa-
ture (long term prediction) of voiced speech signal are modeled and a
linear state space model with unknown parameters is derived. The Expec-
tation Maximization (EM) algorithm is used to estimate these unknown
parameters and therefore help source separation.
Type:
Conference
City:
Saint-Malo
Date:
2010-09-27
Department:
Communication systems
Eurecom Ref:
3215
Copyright:
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in LVA ICA 2010, 9th International Conference on Latent Variable Analysis and Signal Separation, September 27-30, Saint-Malo, France / Also published in LNCS, 2010, Vol 6365/2010 and is available at : http://dx.doi.org/10.1007/978-3-642-15995-4_14
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