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Music-CRN: an Efficient Content-Based Music Classification and Recommendation Network

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Abstract

For human beings, music is generally perceived, categorized, and enjoyed based on its attributes, such as rhythm, pitch, timbre, and harmony. In recent years, due to their high performances, content-based music classification and recommendation systems have attracted much attention from both the music industry and research community. However, on the one hand, deep music classification models are still very rare, and on the other hand, the collaborative filtering approach, which has the cold start problem, still dominates the music recommendation applications. In this paper, we propose Music-CRN (short for music classification and recommendation network), a simple yet effective model that facilitates music classification and recommendation with learning the audio content features of music. Specifically, to extract the content features of music, the audio is converted into spectrogram “images” by Fourier transformation. Music-CRN can be applied on the spectrograms as similar as natural images to effectively extract music content features. Additionally, we collect a new dataset containing nearly 200,000 music spectrogram slices. To the best of our knowledge, this is the first publicly available music spectrogram dataset, which is at https://github.com/YX-Mao/Music-spectrum-image-data. We compare Music-CRN to previous content-based music classification and recommendation models on the collected dataset. Experimental results show that Music-CRN achieves state-of-the-art performance on music classification and recommendation tasks, demonstrating its superiority over previous methods.

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References

  1. Gardini E, Ferrarotti MJ, Cavalli A, Decherchi S. Using principal paths to walk through music and visual art style spaces induced by convolutional neural networks. Cogn Comput. 2021;13(2):570–82.

    Article  Google Scholar 

  2. Liu C, Feng L, Liu G, Wang H, Liu S. Bottom-up broadcast neural network for music genre classification. Multimed Tools Appl. 2021;80(5):7313–31.

    Article  Google Scholar 

  3. Bergstra J, Casagrande N, Erhan D, Eck D, Kégl B. Aggregate features and AdaBoost for music classification. Mach Learn. 2006;65(2–3):473–84.

    Article  Google Scholar 

  4. Cano P, Koppenberger M, Wack N. Content-based music audio recommendation. In: ACM MM. 2005. p. 211–212.

  5. Wang X, Rosenblum DS, Wang, Y. Context-aware mobile music recommendation for daily activities. In: ACM MM. 2012. p. 99–108.

  6. Tzanetakis G, Cook PR. Musical genre classification of audio signals. IEEE Trans Speech and Audio Processing. 2002;10(5):293–302.

    Article  Google Scholar 

  7. Rich E. User modeling via stereotypes. Cogn Sci. 1979;3(4):329–54.

    Article  Google Scholar 

  8. Li S, Kawale J, Fu Y. Deep collaborative filtering via marginalized denoising auto-encoder. In: CIKM. 2015. p. 811–820.

  9. Herlocker JL, Konstan JA, Borchers A, Riedl J. An algorithmic framework for performing collaborative filtering. SIGIR Forum. 2017;51(2):227–34.

    Article  Google Scholar 

  10. Jongpil L, Jiyoung P, Keunhyoung K, Juhan N. SampleCNN: end-to-end deep convolutional neural networks using very small filters for music classification. Appl Sci. 2018;8(1):150.

    Article  Google Scholar 

  11. Choi K, Fazekas G, Sandler MB, Cho K. Transfer learning for music classification and regression tasks. In: ISMIR. 2017. p. 141–149.

  12. Zhong G, Wang H, Jiao W. MusicCNNs: a new benchmark on content-based music recommendation. In: ICONIP. 2018. p. 394–405.

  13. Kour G, Mehan N, Kour G, Mehan N. Music genre classification using MFCC, SVM and BPNN. Int J Comput Appl. 2015;112(6):12–4.

    Google Scholar 

  14. García-García D, Arenas-García J, Parrado-Hernández E, Diaz-de Maria F. Music genre classification using the temporal structure of songs. In: MLSP. 2010. p. 266–271.

  15. McKinney MF, Breebaart J. Features for audio and music classification. In: ISMIR. 2003.

  16. Dieleman S, Brakel P, Schrauwen B. Audio-based music classification with a pretrained convolutional network. In: ISMIR. 2011. p. 669–674.

  17. Bian W, Wang J, Zhuang B, Yang J, Wang S, Xiao J. Audio-based music classification with DenseNet and data augmentation. In: PRICAI. 2019. p. 56–65.

  18. Dieleman S, Schrauwen B. End-to-end learning for music audio. In: ICASSP. 2014. p. 6964–6968.

  19. Kumar V, Khattar D, Gupta S, Gupta M, Varma V. Deep neural architecture for news recommendation. In: CLEF. 2017.

  20. Porteous I, Asuncion AU, Welling M. Bayesian matrix factorization with side information and dirichlet process mixtures. In: AAAI. 2010.

  21. Xiao H, Chen Y, Shi X, Xu G. Multi-perspective neural architecture for recommendation system. Neural Netw. 2019;118:280–8.

    Article  Google Scholar 

  22. Chen J, Zhang H, He X, Nie L, Liu W, Chua T. Attentive collaborative filtering: multimedia recommendation with item- and component-level attention. In: SIGIR. 2017. p. 335–344.

  23. Sánchez-Moreno D, González ABG, Vicente MDM, Batista VFL, García MNM. A collaborative filtering method for music recommendation using playing coefficients for artists and users. Expert Syst Appl. 2016;66:234–44.

    Article  Google Scholar 

  24. Tang YF, Yun YZ, Jin WW, Xiao MC. Music recommendation with collaborative filtering for mobile services. Appl Mech Mater. 2014;519–520:510–5.

    Article  Google Scholar 

  25. Yan Y, Liu T, Wang Z. A music recommendation algorithm based on hybrid collaborative filtering technique. In: SMP, vol. 568. Springer; 2015. p. 233–240.

  26. Liang Z, Tan Z, Zhuo Z, Zhang X. A hybrid music recommendation system based on scene-state perception model. In: ICSCC, vol. 10699. 2017. p. 19–26.

  27. Wang, L. Design and implementation of hybrid music recommendation system based on music gene. In: CIPAE. 2020. p. 121–124.

  28. Wu, D. Music personalized recommendation system based on hybrid filtration. In: ICITBS. 2019. p. 430–433.

  29. Liang D, Zhan M, Ellis DPW. Content-aware collaborative music recommendation using pre-trained neural networks. In: ISMIR. 2015. p. 295–301.

  30. Su J, Chiu T. An item-based music recommender system using music content similarity. In: ACIIDS, vol. 9622. 2016. p. 179–190.

  31. Lin M, Chen Q, Yan S. Network in network. In: ICLR. 2014.

  32. Murray M. Building a music recommender with deep learning. 2017.

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Funding

This work was partially supported by the National Key Research and Development Program of China under Grant No. 2018AAA0100400, the Natural Science Foundation of Shandong Province under Grants No. ZR2020MF131 and No. ZR2021ZD19, and the Science and Technology Program of Qingdao under Grant No. 21-1-4-ny-19-nsh.

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Authors and Affiliations

  1. College of Computer Science and Technology, Ocean University of China, Qingdao, China

    Yuxu Mao, Guoqiang Zhong & Haizhen Wang

  2. Data Science Research Center & Division of Natural and Applied Sciences, Duke Kunshan University, Suzhou, China

    Kaizhu Huang

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  1. Yuxu Mao
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  2. Guoqiang Zhong
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  3. Haizhen Wang
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Correspondence to Guoqiang Zhong.

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Mao, Y., Zhong, G., Wang, H. et al. Music-CRN: an Efficient Content-Based Music Classification and Recommendation Network. Cogn Comput 14, 2306–2316 (2022). https://doi.org/10.1007/s12559-022-10039-x

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  • DOI: https://doi.org/10.1007/s12559-022-10039-x

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