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JBE, vol. 22, no. 6, pp.693-701, November, 2017


Music Genre Classification using Spikegram and Deep Neural Network

Woo-Jin Jang, Ho-Won Yun, Seong-Hyeon Shin, Hyo-Jin Cho, Won Jang and Hochong Park

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In this paper, we propose a new method for music genre classification using spikegram and deep neural network. The human auditory system encodes the input sound in the time and frequency domain in order to maximize the amount of sound information delivered to the brain using minimum energy and resource. Spikegram is a method of analyzing waveform based on the encoding function of auditory system. In the proposed method, we analyze the signal using spikegram and extract a feature vector composed of key information for the genre classification, which is to be used as the input to the neural network. We measure the performance of music genre classification using the GTZAN dataset consisting of 10 music genres, and confirm that the proposed method provides good performance using a low-dimensional feature vector, compared to the current state-of-the-art methods.

Keyword: music genre, genre classification, spikegram, deep neural network

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