11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

A Factorial Sparse Coder Model for Single Channel Source Separation

Robert Peharz (1), Michael Stark (1), Franz Pernkopf (1), Yannis Stylianou (2)

(1) Technische Universität Graz, Austria
(2) University of Crete, Greece

We propose a probabilistic factorial sparse coder model for single channel source separation in the magnitude spectrogram domain. The mixture spectrogram is assumed to be the sum of the sources, which are assumed to be generated frame-wise as the output of sparse coders plus noise. For dictionary training we use an algorithm which can be described as non-negative matrix factorization with l0 sparseness constraints. In order to infer likely source spectrogram candidates, we approximate the intractable exact inference by maximizing the posterior over a plausible subset of solutions. We compare our system to the factorial-max vector quantization model, where the proposed method shows a superior performance in terms of signal-to-interference ratio. Finally, the low computational requirements of the algorithm allows close to real time applications.

Full Paper

Bibliographic reference.  Peharz, Robert / Stark, Michael / Pernkopf, Franz / Stylianou, Yannis (2010): "A factorial sparse coder model for single channel source separation", In INTERSPEECH-2010, 386-389.