A general framework for online audio source separation

Details A general framework for online audio source separation A general framework for online audio source separation

2011-12-28

arxiv.org/abs/1112.6178v1

Computer Science

Sound

International conference on Latente Variable Analysis and Signal Separation (2012)

Scientific paper

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.

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