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Indexed by:期刊论文

Date of Publication:2021-03-05

Journal:IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING

Volume:28

Page Number:3095-3107

ISSN No.:2329-9290

Key Words:Acoustic sensor network; multiple signal classification; sampling rate calibration

Abstract:The sampling rate mismatches among nodes severely degrade the performances of distributed signal processing methods in acoustic sensor networks. An active sampling rate calibration method based on the multiple signal classification (MUSIC) algorithm is proposed to solve this problem. Specifically, a pre-defined sinusoidal signal is used as the calibration sound source, and the sampling rate mismatch problem is formulated based on the Hadamard product between output signals of pairwise nodes. The Hadamard product is then filtered through the pre-designed filters to obtain the target sinusoidal signals whose frequencies linearly correlate with the sampling rate mismatch. Finally, the sampling rate differences among nodes are estimated by the root-MUSIC algorithm and compensated by the sinc-interpolation. The proposed method can effectively calibrate the sampling rate mismatch even under severe noise and reverberation. Moreover, it has a strong tolerance for deviations among the microphone frequency responses. Experimental results reveal the validity of the proposed method.