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On the Use of Latent Mixing Filters in Audio Source Separation

L. Girin #1, Roland Badeau #2
#1 Grenoble Images Parole Signal Automatique (GIPSA-lab)
  • CNRS : UMR5216
  • Université Joseph Fourier - Grenoble I
  • Université Pierre Mendès-France - Grenoble II
  • Université Stendhal - Grenoble III
  • Institut Polytechnique de Grenoble - Grenoble Institute of Technology
#2 Laboratoire traitement et communication de l'information (LTCI)
  • Télécm ParisTech
  • Institut Mines-Télécom
  • Université Paris-Saclay
References
13th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2017), Grenoble, France, February 2017, vol. LNCS 10169, pp. 225--235
Abstract

In this paper, we consider the underdetermined convolutive audio source separation (UCASS) problem. In the STFT domain, we consider both source signals and mixing filters as latent random variables, and we propose to estimate each source image, i.e. each individual source-filter product, by its posterior mean. Although, this is a quite straightforward application of the Bayesian estimation theory, to our knowledge, there exist no similar study in the UCASS context. In this paper, we discuss the interest of this estimator in this context and com- pare it with the conventional Wiener filter in a semi-oracle configuration.

Keywords
Audio source separation, source image, latent mixing filters, MMSE estimator, MCMC sampling.
Category
Paper in proceedings
Research Area(s)
Engineering Sciences/Signal and Image processing
Identifier(s)
Bibliographic key LG:LVAICA-17
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Last update
on march 20, 2017 by Roland Badeau


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