Multichannel Source Separation and Tracking With RANSAC and Directional Statistics

We describe multichannel blind source separation and tracking algorithms based on clustering wrapped interchannel phase difference (IPD) features. We pose the clustering problem as one of multimodal circular-linear regression and present its probabilistic formulation. Phase wrapping due to spatial aliasing is explicitly incorporated by modeling the IPD features as circular variables. We present two methods based on Expectation-Maximization (EM) and a sequential variant of RANdom SAmple Consensus (RANSAC). We show that their strengths can be combined by using RANSAC to initialize EM. The IPD clustering algorithm is applied to separate stationary speakers from a multichannel mixture. We then extend it to the case of moving speakers by tracking their directions-of-arrival with the Factorial Wrapped Kalman Filter (FWKF) using RANSAC as a data preprocessor. Experimental results demonstrate that the proposed methods perform well in the presence of reverberant babble noise and spatial aliasing. The FWKF successfully tracks and separates moving speakers with separation quality comparable to that for stationary speakers.