Abstract
Music classification and recommendation have received wide-spread attention in recent years. However, content-based deep music classification approaches are still very rare. Meanwhile, existing music recommendation systems generally rely on collaborative filtering. Unfortunately, this method has serious cold start problem. In this paper, we propose a simple yet effective convolutional neural network named MCRN (short for music classification and recommendation network), for learning the audio content features of music, and facilitating music classification and recommendation. Concretely, to extract the content features of music, the audio is converted into “spectrograms” by Fourier transform. MCRN can effectively extract music content features from the spectrograms. Experimental results show that MCRN outperforms other compared models on music classification and recommendation tasks, demonstrating its superiority over previous approaches.
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Acknowledgments
This work was supported by the Major Project for New Generation of AI under Grant No.2018AAA0100400, the National Natural Science Foundation of China (NSFC) under Grant No.41706010, the Joint Fund of the Equipments Pre-Research and Ministry of Education of China under Grant No.6141A020337, and the Fundamental Research Funds for the Central Universities of China.
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Mao, Y., Zhong, G., Wang, H., Huang, K. (2020). MCRN: A New Content-Based Music Classification and Recommendation Network. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_88
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DOI: https://doi.org/10.1007/978-3-030-63820-7_88
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