Classification of Sound Signals via Computationally Efficient Supervised and Unsupervised Learning Schemes

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dc.contributor.ORCID0000-0002-3002-675X (Saki, F)
dc.contributor.advisorKehtarnavaz, Nasser
dc.creatorSaki, Fatemeh
dc.date.accessioned2018-05-31T17:31:34Z
dc.date.available2018-05-31T17:31:34Z
dc.date.created2017-05
dc.date.issued2017-05
dc.date.submittedMay 2017
dc.date.updated2018-05-31T17:31:34Z
dc.description.abstractClassification of sound signals is increasingly being used in hearing improvement devices such as hearing aids, cochlear implants, and smart headphones. Classification of sound signals enables adapting the speech enhancement/noise reduction algorithms in such devices to different sound environments in an automatic manner. The thrust of this dissertation research has been on the development of sound signal classification approaches that are computationally efficient, thus enabling their real-time deployment in hearing improvement devices. Both supervised and unsupervised learning schemes have been examined. For the supervised case, effective and computationally efficient features and classifiers have been developed. For the unsupervised case, an online clustering algorithm has been developed without knowing the number of clusters. Experimental results obtained indicate that the developed classification approaches outperform the existing sound classification approaches in terms of both classification rates and computational efficiency.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10735.1/5779
dc.language.isoen
dc.rights©2017 The Author. Digital access to this material is made possible by the Eugene McDermott Library. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
dc.subjectSupervised learning (Machine learning)
dc.subjectComputational auditory scene analysis
dc.subjectSound—Classification
dc.titleClassification of Sound Signals via Computationally Efficient Supervised and Unsupervised Learning Schemes
dc.typeDissertation
dc.type.materialtext
thesis.degree.departmentElectrical Engineering
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.levelDoctoral
thesis.degree.namePHD

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