Blind source separation based on independent low-rank matrix analysis with sparse regularization for time-series activity

Abstract

In this paper, we propose a new blind source separation (BSS) method based on independent low-rank matrix analysis (ILRMA) with novel sparse regularization. ILRMA is a recently proposed BSS algorithm that simultaneously estimates a demixing matrix and source spectrogram models based on nonnegative matrix factorization (NMF). To improve the separation accuracy and stability, an additional constraint such as sparseness is needed but there have been no studies on this so far. In this study, we introduce an a priori statistical model for time-series amplitudes of source spectrograms, employing a new frequency-wise sparse regularization using estimates from the Bayesian postfilter to enhance the modeling accuracy. This regularization results in a bilevel optimization problem that consists of the estimation of a sparsity-emphasized source model using NMF and the separation of sources by ILRMA. In this paper, we present two approximated optimization schemes and their combination for performing regularized ILRMA. The efficacy of the proposed method is confirmed in a BSS experiment.