Validity of the independence assumption for the separation of instantaneous and convolutive mixtures of speech and music sources

Matthieu Puigt 1 Emmanuel Vincent 2 Yannick Deville 1
2 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : In this paper, we study the validity of the assumption that speech source signals exhibit lower dependency and therefore better separability with Independent Component Analysis algorithms than music sources. In particular, we investigate some dependency measures in the temporal and the time-frequency domains, resp. in the framework of instantaneous and convolutive mixtures. Moreover, we test several ICA methods, based on the above dependency measures, on the same source signals. We experimentally show that speech and music sources tend to have the same mean behaviour for excerpt durations above 20~ms, but music signals provide more spread dependency measures and SIR values. Lastly, we experimentally show that Gaussian nonstationary mutual information is better suited to audio signals than mutual information.
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Matthieu Puigt, Emmanuel Vincent, Yannick Deville. Validity of the independence assumption for the separation of instantaneous and convolutive mixtures of speech and music sources. 8th Int. Conf. on Independent Component Analysis and Signal Separation (ICA), Mar 2009, Paraty, Brazil. pp.613--620. ⟨inria-00544163⟩

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