Abstract
In this paper, we propose a new deep convolutional neural network for content-based music recommendation, and call it MusicCNNs. To learn effective representations of the music segments, we have collected a data set including 600,000+ songs, where each song has been split into about 20 music segments. Furthermore, the music segments are converted to “images” using the Fourier transformation, so that they can be easily fed into MusicCNNs. On this collected data set, we compared MusicCNNs with other existing methods for content-based music recommendation. Experimental results show that MusicCNNs can generally deliver more accurate recommendations than the compared methods. Therefore, along with the collected data set, MusicCNNs can be considered as a new benchmark for content-based music recommendation.
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Acknowledgment
This work was supported by the National Key R&D Program of China under Grant 2016YFC1401004, the National Natural Science Foundation of China (NSFC) under Grant No. 41706010, the Science and Technology Program of Qingdao under Grant No. 17-3-3-20-nsh, the CERNET Innovation Project under Grant No. NGII20170416, and the Fundamental Research Funds for the Central Universities of China.
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Zhong, G., Wang, H., Jiao, W. (2018). MusicCNNs: A New Benchmark on Content-Based Music Recommendation. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11301. Springer, Cham. https://doi.org/10.1007/978-3-030-04167-0_36
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DOI: https://doi.org/10.1007/978-3-030-04167-0_36
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