BibTeX
@MISC{Févotte_bayesianaudio,
author = {Cédric Févotte},
title = {Bayesian audio source separation},
year = {}
}
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Abstract
In this chapter we describe a Bayesian approach to audio source separation. The approach relies on probabilistic modeling of sound sources as (sparse) linear combinations of atoms from a dictionary and Markov chain Monte Carlo (MCMC) inference. Several prior distributions are considered for the source expansion coefficients. We first consider independent and identically distributed (iid) general priors with two choices of distributions. The first one is the Student t, which is a good model for sparsity when the shape parameter has a low value. The second one is a hierarchical mixture distribution; conditionally upon an indicator variable, one coefficient is either set to zero or given a normal distribution, whose variance is in turn given an inverted-Gamma distribution. Then, we consider more audio-specific models where both the identically distributed and independently distributed assumptions are lifted. Using a Modified Discrete Cosine Transform (MDCT) dictionary, a time-frequency orthonormal basis, we describe frequency-dependent structured priors which explicitly model the harmonic structure of sound, using a Markov hierarchical modeling of the expansion coefficients. Separation results are given for a stereophonic recording of 3 sources. 1
Keyphrases
bayesian audio source separation sound source good model indicator variable markov hierarchical modeling time-frequency orthonormal basis source expansion coefficient general prior shape parameter frequency-dependent structured prior first one hierarchical mixture distribution separation result harmonic structure stereophonic recording modified discrete cosine transform second one low value probabilistic modeling audio-specific model source separation normal distribution markov chain monte carlo inverted-gamma distribution expansion coefficient bayesian approach several prior distribution
