Abstract
We describe a new method of blind source separation (BSS) on a microphone array combining subband independent component analysis (ICA) and beamforming. The proposed array system consists of the following three sections: (1) subband ICA-based BSS section with estimation of the direction of arrival (DOA) of the sound source, (2) null beamforming section based on the estimated DOA, and (3) integration of (1) and (2) based on the algorithm diversity. Using this technique, we can resolve the low-convergence problem through optimization in ICA. To evaluate its effectiveness, signal-separation and speech-recognition experiments are performed under various reverberant conditions. The results of the signal-separation experiments reveal that the noise reduction rate (NRR) of about 18 dB is obtained under the nonreverberant condition, and NRRs of 8 dB and 6 dB are obtained in the case that the reverberation times are 150 milliseconds and 300 milliseconds. These performances are superior to those of both simple ICA-based BSS and simple beamforming method. Also, from the speech-recognition experiments, it is evident that the performance of the proposed method in terms of the word recognition rates is superior to those of the conventional ICA-based BSS method under all reverberant conditions.
Highlights
Source separation for acoustic signals is to estimate original sound source signals from the mixed signals observed in each input channel
Signal-separation experiments are conducted using the sound data convolved with the impulse responses recorded in two environments specified by different reverberation times (RTs)
We used the following signals as the source signals: (1) the original speech not convolved with the room impulse responses and (2) the original speech convolved with the room impulse responses recorded in the two environments specified by the different RTs
Summary
Source separation for acoustic signals is to estimate original sound source signals from the mixed signals observed in each input channel. Blind source separation (BSS) is the approach to estimate original source signals using only the information of the mixed signals observed in each input channel, where the independence among the source signals is mainly used for the separation This technique is based on unsupervised adaptive filtering [13] and provides us with extended flexibility in which the sourceseparation procedure requires no training sequences and no a priori information on DOAs of the sound sources. A new subband ICA is introduced to achieve frequency domain BSS on the microphone array system, where directivity patterns of the array are explicitly used to estimate each DOA of the sound sources [22] Using this method, we can resolve both permutation and arbitrariness problems simultaneously without the assumption for the source signal waveforms or interfrequency continuity of the unmixing matrices.
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