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Audio Classification using Gaussian Mixture Model

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dc.contributor.author Myint, Yuzana
dc.contributor.author Swe, Ei Mon Mon
dc.date.accessioned 2019-08-02T06:18:26Z
dc.date.available 2019-08-02T06:18:26Z
dc.date.issued 2009-08-03
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1615
dc.description.abstract This paper describes the work done on the development of an audio classification system. Audio recordings are classified into basic audio types such as speech or music. Audio classification is processed in two parts, feature extraction and classification, which makes it suitable for different applications. In first part, input audio signal is transformed into feature vectors, carrying characteristics information about the signal. Audio Features for this task include Short Time Energy (SE), Zero Crossing Rate (ZC) and Mel Frequency Cepstral Coefficient (MFCC). The second part classify feature vector into speech or music. Gaussian Mixture Model (GMM) is used for classification task. The overall accuracy rate of this paper gets around 93%. en_US
dc.language.iso en en_US
dc.publisher Third Local Conference on Parallel and Soft Computing en_US
dc.title Audio Classification using Gaussian Mixture Model en_US
dc.type Article en_US


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