Sound source segregation based on estimating incident angle of each frequency component of input signals acquired by multiple microphones

Acoustical Science and Technology

Manabu Okamoto²  Mariko Aoki⁵  Shigeaki Aoki⁴  Yutaka Kaneda⁶  Hiroyuki Matsui¹  Tetsuma Sakurai³ 

[1] Solution Business Division, NTT Communications Corporation, Kowa Nishi-shinbashi Bldg. B Tower 14-1, Nishi-shinbashi 2-chome, Minato-ku, Tokyo, 105-0003 Japan;Business Communications Headquarters, NTT East Corporation, UrbanNet Otemachi Bldg., 2-2-2 Otemachi, Chiyoda-ku, Tokyo, 100-0004 Japan;Department of Information Science, Faculty of Engineering, Fukui University, 9-1, Bunkyo 3-chome, Fukui, 910-8507 Japan;Media Technology Development Center, NTT Communications Corporation, Tokyo Opera City Tower 21F, 3-20-2 Nishi-Shinjuku, Shinjuku-ku, Tokyo, 163-1421 Japan;Media Processing Project, NTT Cyber Space Laboratories, 3-9-11 Midorichou. Musasino, 180-8585 Japan;Department of Information and Communication Engineering, Tokyo Denki University, 2-2, Kanda-Nishiki-cho Chiyoda-ku, Tokyo, 101-8457 Japan

关键词: Sound source segregation;    Phase difference between input signals;    Amplitude difference between input signals;    Frequency analysis;    Discrete Fourier transformation;   

DOI  :  10.1250/ast.22.149

学科分类：声学和超声波

来源: Acoustical Society of Japan

【 摘 要 】

References(12)Cited-By(38)We have developed a method of segregating desired speech from concurrent sounds received by two microphones. In this method, which we call SAFIA, signals received by two microphones are analyzed by discrete Fourier transformation. For each frequency component, differences in the amplitude and phase between channels are calculated. These differences are used to select frequency components of the signal that come from the desired direction and to reconstruct these components as the desired source signal. To clarify the effect of frequency resolution on the proposed method, we conducted three experiments. First, we analyzed the relationship between frequency resolition and the power spectrum’s cumulative distribution. We found that the speech-signal power was concentrated on specific frequency components when the frequency resolution was about 10-Hz. Second, we determined whether a given frequency resolution decreased the overlap between the frequency components of two speech signals. A 10-Hz frequency resolution minimized the overlap. Third, we analyzed the relationship between sound quality and frequency resolution through subjective tests. The best frequency resolution in terms of sound quality corresponded to the frequency resolutions that concentrated the speech signal power on specific frequency components and that minimized the degree of overlap. Finally, we demonstrated that this method improved the signal-to-noise ratio by over 18dB.

【 授权许可】

Unknown