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Music feature extraction and genres form a natural way to consolidate audio and they share related rhythm and texture. We will be building a customizedfeature extraction genre classification model using customized kernel in support vector machine that will use features representing timbre, rhythmic and pitch analysis of the audio. We train various classifiers like k-Nearest neighbor, Support vector machine, Logistic Regression, Neural Network on the GTZAN dataset provided by MARYSAS. We are able to get good accuracy using Customized kernel and ensemble voting classifier and support vector machine on both 10-genre and 4-genre classification.
Journal Type : Uluslararası
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