Blind Separation For Multiple Moving Sources With Labeled Random Finite Sets

This paper proposes a novel solution for separating an unknown and time-varying number of moving acoustic sources in a blind setting using multiple microphone arrays. A standard steered-response power phase transform method is applied to extract source position measurements, which inevitably contain noise, false detections, missed detections, and are not labeled with the source identities. The imperfect measurements lead to the space-time permutation problem, as there is no information on how the measurements are associated to the sources in space, nor how the measurements are connected across time, if at all. To solve this problem, a labeled random finite set tracking framework is adopted to jointly estimate the source positions and their labels or identities. Based on these trajectory estimates, a corresponding set of time-varying generalized side-lobe cancellers is constructed to perform source separation. The overall algorithm operates in a block-wise or an online fashion and is scalable with the number of microphone arrays. The quality of the measurements, tracking, and separation, are evaluated respectively, with the OSPA metric, OSPA((2)) metric, and ITU-T P.835 based listening tests, on both real-world and simulated data.