Post-Processing for the Mask of Computational Auditory Scene Analysis in Monaural Speech Segregation

Wen-Hsing Lai, Cheng-Jia Yang, Siou-Lin Wang


Speech segregation is one of the most difficult tasks in speech processing. This paper uses computational auditory scene analysis, support vector machine classifier, and post-processing on binary mask to separate speech from background noise. Melfrequency cepstral coefficients and pitch are the two features used for support vector machine classification. Connected Component Labeling, Hole Filling, and Morphology are applied on the resulting binary mask as post-processing. Experimental results show that our method separates speech from background noise effectively.


CASA, Connected Component Labeling, SVM

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