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We cast the under-determined convolutive speech separation as sparse approximation of the spatial spectra of the mixing sources. In this framework we compare and contrast the major practical algorithms for structured sparse recovery of speech signal. Specific attention is paid to characterization of the measurement matrix. We first propose how it can be identified using the Image model of multipath...
We leverage the recent algorithmic advances in compressive sensing, and propose a novel source separation algorithm for efficient recovery of convolutive speech mixtures in spectro-temporal domain. Compared to the common sparse component analysis techniques, our approach fully exploits structured sparsity models to obtain substantial improvement over the existing state-of-the-art. We evaluate our...
In this paper, we address the localization of simultaneous speakers by means of Blind Source Separation (BSS) based algorithms. Considering BSS demixing filters as some blind null beamformer and producing an acoustical map from them, source localization can be achieved by identifying the local minima of this acoustical map. To improve the performance of this method in reverberant environments, we...
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