Thu-3-11-1 Sparseness-Aware DOA Estimation with Majorization Minimization

Masahito Togami(Line Corporation) and Robin Scheibler(LINE)
Abstract: We propose a direction-of-arrival (DOA) estimation technique which assumes that speech sources are sufficiently sparse and there is only one active speech source at each time-frequency (T-F) point. The proposed method estimates the DOA of the active speech source at each T-F point. A typical way for DOA estimation is based on grid-searching for all possible directions. However, computational cost of grid-searching is proportional to the resolution of search area. Instead of accurate grid-searching, the proposed method adopts rough grid-searching followed by an iterative parameter optimization based on Majorization-Minimization (MM) algorithm. We propose a parameter optimization method which guarantees a monotonical increase of the objective function. Experimental results show that the proposed method estimates DOAs of speech sources more accurately than conventional DOA estimation methods when computational cost of each method is almost the same.
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