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PITCH ESTIMATION FRAMEWORK FOR SPEECH SEGREGATION USING COCHLEAGRAM MORPHING

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dc.contributor.author Khan, M.J
dc.contributor.author Habib, H.A
dc.date.accessioned 2022-10-26T10:04:16Z
dc.date.available 2022-10-26T10:04:16Z
dc.date.issued 2015-12-10
dc.identifier.citation Khan, M. J., & Habib, H. A. (2015). PITCH ESTIMATION FRAMEWORK FOR SPEECH SEGREGATION USING COCHLEAGRAM MORPHING. Pakistan Journal of Science, 67(4). en_US
dc.identifier.issn 2411-0930
dc.identifier.uri http://142.54.178.187:9060/xmlui/handle/123456789/13752
dc.description.abstract Computational auditory scene analysis (CASA) has significant role in speech segregation from monaural audio mixtures and generally a measure for performance of speech recognition systems. Pitch estimation has a substantial role in performance of CASA systems. This study presents a novel pitch estimation framework for speech segregation from monaural audio mixtures using cochleagram morphing. The proposed framework takes the rough estimation of target pitch from given audio mixtures containing speech and background interferences. Discrete set consisting morphed versions of cochleagram is obtained using k-Means clustering. The estimated pitch values are improved by validating and smoothing them to morphed cochleagram. Measure of refined estimated pitch contours along with harmonicity and temporal continuity are used to segregate target speech. The proposed framework produced 83.13% accuracy for MIR-1k dataset which is considerably higher than the existing methods en_US
dc.language.iso en en_US
dc.publisher Lahore:Pakistan Association for the Advancement of Science en_US
dc.subject Pitch range estimation en_US
dc.subject Source separation en_US
dc.subject Computer auditory scene analysis (CASA) en_US
dc.subject k-means; Spectral Peaks. en_US
dc.title PITCH ESTIMATION FRAMEWORK FOR SPEECH SEGREGATION USING COCHLEAGRAM MORPHING en_US
dc.type Article en_US


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