Separation of Reverberant Speech Based on Computational Auditory Scene Analysis
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This paper proposes a computational auditory scene analysis approach to separation of room reverberant speech, which performs multi-pitch tracking and supervised classification. The algorithm trains speech and non-speech model separately, which learns to map from harmonic features to grouping cue encoding the posterior probability of time-frequency unit being dominated by the target and periodic interference. Then, a likelihood ratio test selects the correct model for labeling time-frequency unit. Experimental results show that the proposed approach produces strong pitch tracking results and leads to significant improvements of predicted speech intelligibility and quality. Compared with the classical Jin-Wang algorithm, the average SNR of this algorithm is improved by 1.22 dB.
Keywords:computational auditory scene analysis room reverberant supervised classification harmonic features
This work was supported by Shanxi Natural Science Foundation (no. 201701D121058).
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