Blind Source Separation of Moving Sources Using Sparsity-Based Source Detection and Tracking
Sparsity-based blind source separation (BSS) algorithms in the short time-frequency (TF) domain have received a lot of attention due to their versatility and noise reduction capabilities. In most of these algorithms, the estimation of the BSS filters relies on the accurate association of each time-frequency bin to the dominant source at that bin. The TF bin associations are then used to estimate the statistics of the source signals, and BSS is achieved by optimal spatial filters computed using the estimated statistics. The main objective of this paper is to apply such a framework to scenarios with an unknown number of moving sources. While state-of-the-art approaches employ online clustering algorithms to solve the problem for moving sources, we propose an approximate Bayesian tracker and perform the association of each TF bin to the dominant source using the tracker's measurement-to-source association probabilities. Therefore, the choice of the underlying narrowband models and measurements for the tracker as well as the resulting tracking algorithm constitute the main contributions of this paper. The TF bin associations obtained from the tracker are then used to estimate the statistics of the source signals. The performance of the resulting BSS filters is compared to the performance of state-of-the-art sparsity-based and independent vector analysis-based BSS algorithms. Our proposed approach targets scenarios with at least two spatially separated microphone arrays, with known microphone positions and relative orientations. The framework also allows for efficient management of a time-varying number of sources.