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A general framework for online audio source separation
Abstract : We consider the problem of online audio source separation. Existing algorithms adopt either a sliding block approach or a stochastic gradient approach, which is faster but less accurate. Also, they rely either on spatial cues or on spectral cues and cannot separate certain mixtures. In this paper, we design a general online audio source separation framework that combines both approaches and both types of cues. The model parameters are estimated in the Maximum Likelihood (ML) sense using a Generalised Expectation Maximisation (GEM) algorithm with multiplicative updates. The separation performance is evaluated as a function of the block size and the step size and compared to that of an offline algorithm.
Cited literature [12 references]
https://hal.inria.fr/hal-00655398
Contributor : Laurent S. R. Simon <>
Submitted on : Wednesday, December 28, 2011 - 11:59:09 AM
Last modification on : Thursday, January 7, 2021 - 4:33:50 PM
Long-term archiving on: : Thursday, March 29, 2012 - 2:20:33 AM
Contributor : Laurent S. R. Simon <>
Submitted on : Wednesday, December 28, 2011 - 11:59:09 AM
Last modification on : Thursday, January 7, 2021 - 4:33:50 PM
Long-term archiving on: : Thursday, March 29, 2012 - 2:20:33 AM
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