A Robust Method to Count and Locate Audio Sources in a Stereophonic Linear Instantaneous Mixture

Simon Arberet 1 Rémi Gribonval 1 Frédéric Bimbot 1
1 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : We propose a robust method to estimate the number of audio sources and the mixing matrix in a linear instantaneous mixture, even with more sources than sensors. Our method is based on a multiscale Short Time Fourier Trans- form (STFT), and relies on the assumption that in the neighborhood of some (unknown) scales and time-frequency points, only one source contributes to the mixture. Such time-frequency regions provide local estimates of the correspond- ing columns of the mixing matrix. Our main contribution is a new clustering al- gorithm called DEMIX to estimate the number of sources and the mixing matrix based on such local estimates. In contrast to DUET or other similar sparsity-based algorithms, which rely on a global scatter plot, our algorithm exploits a local confidence measure to weight the influence of each time-frequency point in the estimated matrix. Inspired by the work of Deville, the confidence measure relies on the time-frequency local persistence of the activity/inactivity of each source. Experiments are provided with stereophonic mixtures and show the improved performance of DEMIX compared to K-means or ELBG clustering algorithms.
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Simon Arberet, Rémi Gribonval, Frédéric Bimbot. A Robust Method to Count and Locate Audio Sources in a Stereophonic Linear Instantaneous Mixture. Proc. of the Int'l. Workshop on Independent Component Analysis and Blind Signal Separation (ICA 2006), Mar 2006, Charleston, South Carolina, United States. pp.536--543, ⟨10.1007/11679363_67⟩. ⟨inria-00544925⟩



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