Doa Estimation for Multiple Sparse Sources with Normalized Observation Vector Clustering

Shoko Araki, Hiroshi Sawada, Ryo Mukai, Shoji Makino

Abstract: This paper presents a new method for estimating the direction of arrival (DOA) of source signals whose number N can exceed the number of sensors M. Subspace based methods, e.g., the MUSIC algorithm, have been widely studied, however, they are only applicable when M > N. Another conventional independent component analysis based method allows M ges N, however, it cannot be applied when M < N. By contrast, our new method can be applied where the sources outnumber the sensors (i.e., an underdetermined case M < N) by assuming source sparseness. Our method can cope with 2- or 3-dimensionally distributed sources with a 2- or 3-dimensional sensor array. We obtained promising experimental results for 3 times 4, 3 times 5 and 4 times 5 (#sensors times #speech sources) in a room (RT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">60</sub> = 120 ms)