Source separation by fuzzy-membership value aware beamforming and masking in ad hoc arrays

Inhalt:
This paper presents the concept of fuzzy-membership value (FMV) aware delay-and-sum beamforming for source separation in reverberant environments using ad hoc distributed microphones. Our approach employs a previously proposed fuzzy clustering algorithm to assign microphones of ad hoc arrays to individual source-dominated clusters and to compute fuzzy-membership values for each microphone and cluster. For each source-dominated cluster we first estimate relative time-differences-of-arrival (TDOA) information from the observed microphone signals and then apply both the TDOA and the FMV information in the beamforming stage. We show that such weighted beamforming improves upon the unweighted case. In a second enhancement stage we then apply cluster-related spectral masks to the output of the beamformers. We validate the proposed approach in three realistically-simulated rooms of different sizes. The method is evaluated by informal listening tests as well as by instrumental quality and intelligibility measures.