Abstract
Convolutional neural networks (CNNs) have become increasingly important to deal with many image processing and pattern recognition problems. In order to use CNNs in music genre recognition, spectrograms (visual representation of the spectrum of frequencies of a signal as it varies with time) are usually employed as inputs of the network. Yet some other approaches used music features for genre classification as well. In this paper we propose a new deep network model combining CNN with a simple multi-layer neural network for music genre classification. Since other features are taken into account in the multi-layer network, the combined deep neural network has shown better accuracy than each of the single models in the experiments (Code available at: https://github.com/risengnom/Music-Genre-Recognition.).
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Suero, M., Gassen, C.P., Mitic, D., Xiong, N., Leon, M. (2020). A Deep Neural Network Model for Music Genre Recognition. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_41
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DOI: https://doi.org/10.1007/978-3-030-32456-8_41
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