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Doa Estimation for Multiple Sparse Sources with Normalized Observation Vector Clustering

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4 Author(s)
S. Araki ; NTT Communication Science Laboratories, NTT Corporation, 2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan.; Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo-shi, Hokkaido 060-0814, Japan. Email: ; H. Sawada ; R. Mukai ; S. Makino

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 (RT60= 120 ms)

Published in:

2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings  (Volume:5 )

Date of Conference:

14-19 May 2006