CUED Publications database

A factorial sparse coder model for single channel source separation

Peharz, R and Stark, M and Pernkopf, F and Stylianou, Y (2010) A factorial sparse coder model for single channel source separation. In: UNSPECIFIED pp. 386-389..

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Abstract

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 ℓ 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. © 2010 ISCA. 0

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 18 Oct 2017 20:08
Last Modified: 13 Apr 2021 07:13
DOI: