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

Authors

  • Wen-Hsing Lai Institute of Computer and Communication Engineering Kaohsiung First, University of Science and Technology, Taiwan
  • Cheng-Jia Yang Institute of Computer and Communication Engineering Kaohsiung First, University of Science and Technology, Taiwan
  • Siou-Lin Wang Institute of Computer and Communication Engineering Kaohsiung First, University of Science and Technology, Taiwan

DOI:

https://doi.org/10.13053/cys-21-4-2846

Keywords:

CASA, Connected Component Labeling, SVM

Abstract

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.

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Published

2017-12-23

Issue

Vol. 21 No. 4 (2017): Advances in Human Language Technologies (Guest Editor: A. Gelbukh)

Section

Articles of the Thematic Issue

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