Abstract
A key problem in computational auditory scene analysis (CASA) is monaural speech segregation, which has proven to be very challenging. For monaural mixtures, one can only utilize the intrinsic properties of speech or interference to segregate target speech from background noise. Ideal binary mask (IBM) has been proposed as a main goal of sound segregation in CASA and has led to substantial improvements of human speech intelligibility in noise. This study proposes a classification approach to estimate the IBM and employs support vector machines to classify time-frequency units as either target- or interference-dominant. A re-thresholding method is incorporated to improve classification results and maximize hit minus false alarm rates. An auditory segmentation stage is utilized to further improve estimated masks. Systematic evaluations show that the proposed approach produces high quality estimated IBMs and outperforms a recent system in terms of classification accuracy.
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