Adaptive noise reduction using a cascaded hybrid neural network

dc.contributor.advisorDony, R.
dc.contributor.authorChau, Edward Yu-Ho of Engineeringen_US of Guelphen_US of Scienceen_US
dc.description.abstractThis thesis presents the development of an artificial neural network system for adaptive subband noise reduction, CHN3R (Cascaded Hybrid Neural Network for Noise Reduction). In particular, the proposed system focuses on noise reduction of corrupted speech signals in realistic noisy environments. The intended end user of CHN3R is human listener, for such applications as digital hearing aids and personal communication devices. A literature survey of the myriad noise reduction/cancellation approaches briefly introduces the reader to the current state of the art. CHN 3R is developed for the frequency domain, combining the self-organizing map and radial basis network structures. Its performance is evaluated using three different measures: segmental signal-to-noise ratio, a perceptually weighted distortion measure, as well as an informal subjective listening test. It is also compared with several classical and commercial algorithms. The performance of CHN3R is found to be comparable to some commercial algorithms in terms of the objective measures, but is found to be subjectively preferable over all the other algorithms with which it is compared.en_US
dc.publisherUniversity of Guelphen_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectartificial neural network systemen_US
dc.subjectadaptive subband noise reductionen_US
dc.subjectCascaded Hybrid Neural Network for Noise Reductionen_US
dc.subjectnoise reductionen_US
dc.subjectspeech signalsen_US
dc.subjectnoisy environmentsen_US
dc.subjectsignal-to-noise ratioen_US
dc.subjectdistortion measureen_US
dc.subjectlistening testen_US
dc.titleAdaptive noise reduction using a cascaded hybrid neural networken_US


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