MCRN: A New Content-Based Music Classification and Recommendation Network

Yuxu Mao, Guoqiang Zhong*, Haizhen Wang, Kaizhu Huang

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationNeural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
EditorsHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
ISBN (Print)9783030638191
Publication statusPublished - 2020
Event27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand
Duration: 18 Nov 202022 Nov 2020

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference27th International Conference on Neural Information Processing, ICONIP 2020


  • Convolutional neural networks
  • Information retrieval
  • Music classification and recommendation
  • Music spectrogram dataset


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