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We propose a novel sparse representation for heavily underdeterminedmultichannel sound mixtures, i.e., with much more sources than microphones. Theproposed approach operates in the complex Fourier domain, thus preservingspatial characteristics carried by phase differences. We derive ageneralization of K-SVD which jointly estimates a dictionary capturing bothspectral and spatial features, a sparse activation matrix, and allinstantaneous source phases from a set of signal examples. The dictionary canthen be used to extract the learned signal from a new input mixture. The methodis applied to the challenging problem of ego-noise reduction for robotaudition. We demonstrate its superiority relative to conventionaldictionary-based techniques using recordings made in a real room.

Phase-Optimized K-SVD for Signal Extraction from Underdetermined Multichannel Sparse Mixtures