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

TLDR: A method of segregating desired speech from concurrent sounds received by two microphones that improved the signal-to-noise ratio by over 18dB and clarified the effect of frequency resolution on the proposed method.

Abstract: 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.