Audio environment classification for hearing aids

dc.contributor.advisorDony, R.D.
dc.contributor.authorFreeman, Cecille of Engineeringen_US of Guelphen_US of Scienceen_US
dc.description.abstractThis thesis examines background classification systems for hearing aids. First, the K-nearest neighbours classifiers (KNN), hidden Markov models (HMM), multi-layer perceptrons (MLP) and MLPs with windowed input (WMLP) are assessed for functionality. K-means clustering and self-organizing maps (SOMs) are used to find appropriate classes. The classes selected are in-car, traffic, birds, water washing, water running, office, restaurant, shopping and music. Feature selection is then performed on a large candidate set from literature, using sequential forward floating search (SFFS). The classifiers are then tested using the classes and feature vector selected, and using a simple feature vector from literature. Using features selection improves the results for the KNN, but not the other classifiers. The KNN gives an average accuracy of 77.6% and a best-run accuracy of 84.4%. The WMLP gives an average accuracy of 65.6% and a best-run accuracy of 80.0%, but requires less memory and processing than the KNN.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.subjectclassification systemsen_US
dc.subjecthearing aidsen_US
dc.subjectK-nearest neighbours classifiersen_US
dc.subjecthidden Markov models (HMM)en_US
dc.subjectmulti-layer perceptrons (MLP)en_US
dc.subjectMLPs with windowed input (WMLP)en_US
dc.subjectK-means clusteringen_US
dc.subjectself-organizing maps (SOMs)en_US
dc.subjectsequential forward floating search (SFFS)en_US
dc.titleAudio environment classification for hearing aidsen_US


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