On-line multichannel estimation of source spectral dominance

Abstract

Despite its popularity, multichannel source demixing is intrinsically limited in real-world applications due to the model mismatch between the convolutive mixing model and the actual recordings. Varying number of sources, reverberation, diffuseness and spatial changes are common uncertainties that need to be handled. Post-processing is commonly adopted to compensate for these mismatches, generally in the form of non-linear spectral filtering. In this work we analyze the property of the normalized differences between the output magnitudes of a linear spatial filter. We show that thanks to the time-frequency sparsity of acoustic signals, such distributions can be approximatively modeled by a bimodal Gaussian mixture model. An on-line bimodal constrained GMM fitting is proposed, in order to estimate the posterior probability of source spectral dominance. It is shown that the estimated posteriors can be used to produce a filtered output with very low distortion, outperforming traditional non-linear methods.