COMPARISON OF ENHANCED SCHEMES FOR AUDIO CLASSIFICATION

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dc.categoryJournal Article
dc.contributor.authorRadha, V
dc.date.accessioned2017-03-28T22:49:49Z
dc.date.available2017-03-28T22:49:49Z
dc.date.issued2013
dc.departmentComputer Scienceen_US
dc.description.abstractIn the modem era of communication, audio plays an important role in understanding a digital media. Due to the rise of economical audio capturing devices, the amount of audio data available both online and offline is enormous and techniques that can automatically classify and retrieve these audio data is an immediate need. An automatic content based audio classification and retrieval system consists of three modules namely, feature extraction, classification and retrieval. This paper presents a comparative smdy of two algorithms that performs these three steps in different manners. The performance of the selected systems are analyzed while using four different features (acoustic, percepmal, mel-frequency cepstral coefficients (MFCC) and a ''"mbination of percepmal and MFCC) and four classifiers that enlianced Support Vector Machine (SVM) and Centroid Neural itwork (CNN) along with its base versions, SVM and CNN. Experimental results showed that the enhanced SVM algorithm when using the combined feature vector produced improved accinacy and reduced error rate.en_US
dc.identifier.urihttps://ir.avinuty.ac.in/handle/avu/2252
dc.langEnglishen_US
dc.publisher.nameJournal of Global Research in Computer Scienceen_US
dc.publisher.typeInternationalen_US
dc.titleCOMPARISON OF ENHANCED SCHEMES FOR AUDIO CLASSIFICATIONen_US
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