Over-Determined Source Separation and Localization Using Distributed Microphones

We propose an overdetermined source separation and localization method for a set of M microphones distributed around an unknown number, N <; M, of sources. We reformulate the overdetermined acoustic mixing procedure with a new determined mixing model and apply a determined M × M independent component analysis ('CA) in each frequency bin directly. The reformulated 'CA operates without knowing N and also leads to better separation in reverberant scenarios. To solve the challenging permutation ambiguity problem, we first employ a time activity-based clustering approach to cluster the separated frequency components into M channels. We then propose a remixing procedure to detect and merge channels from the same source. The detection is done by analyzing time and frequency activities, spectral likeliness, and spatial location. To estimate the spatial location, we propose a time-frequency masking-based steered response power algorithm. Simulated and real-data experiments in a very challenging reverberant scenario confirm the effectiveness of the proposed method in obtaining the number of sources, the separated signals, and the location and spatial likelihood of each source.