A study of the effect of source sparsity for various transforms on blind audio source separation performance

In this paper, the problem of blind separation of underdetermined noisy mixtures of audio sources is considered. The sources are as-sumed to be sparsely represented in a transform domain. The spar-sity of their analysis coefficients is modelled by the Student t dis-tribution. This prior allows for robust Bayesian estimation of the sources, the mixing matrix, the additive noise variance as well as hyperparameters of the Student t priors, using a Gibbs sampler (a standard Monte Carlo Markov Chain simulation method). The per-formances resulting from the use of various transforms, orthonor-mal as well as overcomplete, are compared. More precisely, we present extensive separation results of 2 × 3 mixtures of various sets of sources (speech, musical and percussion sources) using variou