Multi-speaker voice activity detection by an improved multiplicative non-negative independent component analysis with sparseness constraints

Abstract

We propose an improved version of the non-negative independent component analysis algorithm that uses a multiplicative update rule (M-NICA). We examine a challenging NICA application in a noise-embedded multi-speaker voice activity detection (VAD) setup. We present a novel approach that includes sparsity constraints to solve the energy separation problem with independent source signals. A sparse feature extraction step is performed to project the non-negative signals onto a dimension-reduced subspace and identify sparse principal components. Then, we maximize the signal decorrelation by employing a median measure of central tendency in the computation of the covariance matrix that contributes in robustness against outliers. Moreover, our approach supplies a straightforward multi-speaker VAD, for which no empirical thresholding or other ad-hoc decision rule is required. Instead, an active voice frame simply corresponds to a non-zero value of the separated energy signal. Numerical experiments using real data validate the superior performance of the proposed technique.