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ICASSP
2011
IEEE

An SVM based classification approach to speech separation

7 years 9 months ago
An SVM based classification approach to speech separation
Monaural speech separation is a very challenging task. CASAbased systems utilize acoustic features to produce a time-frequency (T-F) mask. In this study, we propose a classification approach to monaural separation problem. Our feature set consists of pitchbased features and amplitude modulation spectrum features, which can discriminate both voiced and unvoiced speech from nonspeech interference. We employ support vector machines (SVMs) followed by a re-thresholding method to classify each T-F unit as either targetdominated or interference-dominated. An auditory segmentation stage is then utilized to improve SVM-generated results. Systematic evaluations show that our approach produces high quality binary masks and outperforms a previous system in terms of classification accuracy.
Kun Han, DeLiang Wang
Added 20 Aug 2011
Updated 20 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Kun Han, DeLiang Wang
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