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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Optimizing the Objective Measure of Speech Quality in Monaural Speech Separation

verfasst von : M. Dharmalingam, M. C. John Wiselin, R. Rajavel

Erschienen in: Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics

Verlag: Springer India

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Abstract

Monaural speech separation based on computational auditory scene analysis (CASA) is a challenging problem in the field of signal processing. The Ideal Binary Mask (IBM) proposed by DeLiang Wang and colleague is considered as the benchmark in CASA. However, it introduces objectionable distortions called musical noise and moreover, the perceived speech quality is very poor at low SNR conditions. The main reason for the degradation of speech quality is binary masking, in which some part of speech is discarded during synthesis. In order to address this musical noise problem in IBM and improve the speech quality, this work proposes a new soft mask as the goal of CASA. The performance of the proposed soft mask is evaluated using perceptual evaluation of speech quality (PESQ). The IEEE speech corpus and NOISEX92 noises are used to conduct the experiment. The experimental results indicate the superior performance of the proposed soft mask as compared to the traditional IBM in the context of monaural speech separation.

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Metadaten
Titel
Optimizing the Objective Measure of Speech Quality in Monaural Speech Separation
verfasst von
M. Dharmalingam
M. C. John Wiselin
R. Rajavel
Copyright-Jahr
2016
Verlag
Springer India
Buch
Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics

Print ISBN: 978-81-322-2537-9

Electronic ISBN: 978-81-322-2538-6

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-81-322-2538-6_56

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