Feature extraction and genre-classification using customized kernel for Music information retrieval
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Abstract
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.
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