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A Versatile Framework for Speaker Separation Using a Model-Based Speaker Localization Approach

We build upon our speaker localization framework developed in a previous work (N. Madhu and R. Martin, A scalable framework for multiple speaker localization and tracking,” in Proc. Int. Workshop Acoustic Echo Noise Control (IWAENC), Sep. 2008) to perform source separation. The proposed approach, exploiting the supplementary information from the mixture of Gaussians-based localization model, allows for the incorporation of a wide class of separation algorithms, from the nonlinear time-frequency mask-based approaches to a fully adaptive beamformer in the generalized sidelobe canceller (GSC) structure. We propose, in addition, a generalized estimation of the blocking matrix based on subspace projectors. The adaptive beamformer realized as proposed is insensitive to gain mismatches among the sensors, obviating the need for magnitude calibration of the microphones. It is also demonstrated that the proposed linear approach has a performance comparable to that of an optimal (oracle) GSC implementation. In comparison to ICA-based approaches, another advantage of the separation framework described herein is its robustness to ambient noise and scenarios with an unknown number of sources.