NON-SQUARE BLIND SOURCE SEPARATION UNDER COHERENT NOISE BY BEAMFORMING AND TIME-FREQUENCY MASKING

To be applicable in realistic scenarios, blind source separation approaches should deal evenly with non-square cases and the presence of noise. We consider an additive noise mixing model with an arbitrary number of sensors and possibly more sources than sensors (the non-square case) when sources are disjointly orthogonal. We formulate the maximum likelihood estimation of the coherent noise model, suitable when sensors are nearby and the noise field is close to isotropic, and also under the direct-path far-field assumptions. The implementation of the derived criterion involves iterating two steps: a partitioning of the time-frequency plane for separation followed by an optimization of the mixing parameter estimates. The structure of the solution is surprising at first but logical: it consists of a beamforming linear filter, which reduces noise, and a filter across time-frequency domain to separate sources. The solution is applicable to an arbitrary number of microphones and sources. Experimentally, we show the capability of the technique to separate four voices from two, four, six, and eight channel recordings in the presence of isotropic noise.

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