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Music Track Recommendation Using Deep-CNN and Mel Spectrograms

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Abstract

Recommender systems using IoT and deep learning play a vital part in creating an engaging experience on online music streaming platforms. However, in the musical domain, it is quite challenging to build a recommender system as some of the tracks are short. Similarly, some are listened to several times or generally consumed in sessions with other tracks. The recommendation of the next track is highly context dependent. Traditional recommendation algorithms were not able to extract deep-level features from the audio signal and effectively mine user’s preferred music. Therefore, this paper aims to propose a deep learning-based model to build a music recommendation algorithm. The algorithm first preprocesses the original data, and then generates the Mel spectrogram feature set through fast Fourier transform and Mel filter processing. After applying logarithmic operation, these spectrograms are then fed to the convolutional neural network algorithm to categorize music tracks. The inference results are used to understand the user’s preferences and recommend their favorite music tracks. Experimental research and comparison on different data sets show that the algorithm has good performance in the recommendation effect.

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Data Availability

The corresponding author can provide the datasets used and analyzed during the current study upon reasonable request.

References

  1. Schedl M, Zamani H, Chen C-W, Deldjoo Y, Elahi M (2018) “Current challenges and visions in music recommender systems research,” International Journal of Multimedia Information Retrieval, vol. 7, no. 2, pp. 95–116, Jun. doi: https://doi.org/10.1007/s13735-018-0154-2

  2. Darshna P (2018) “Music recommendation based on content and collaborative approach & reducing cold start problem,” in 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore: IEEE, Jan. 2018, pp. 1033–1037. doi: https://doi.org/10.1109/ICISC.2018.8398959

  3. Mao K, Chen G, Hu Y, Zhang L (Mar. 2016) Music recommendation using graph based quality model. Sig Process 120:806–813. https://doi.org/10.1016/j.sigpro.2015.03.026

  4. Shakirova E (2017) “Collaborative filtering for music recommender system,” in IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), St. Petersburg and Moscow, Russia: IEEE, 2017, pp. 548–550. doi: https://doi.org/10.1109/EIConRus.2017.7910613

  5. Thorat PB, Goudar R, Barve S (Jan. 2015) Survey on collaborative filtering, content-based Filtering and Hybrid Recommendation System. Int J Comput Appl 110:31–36. https://doi.org/10.5120/19308-0760

  6. Zhang S, Yao L, Sun A, Tay Y (Jan. 2020) Deep learning based Recommender System: a Survey and New Perspectives. ACM-CSUR 52(1):1–38. https://doi.org/10.1145/3285029

  7. Ludewig M, Kamehkhosh I, Landia N, Jannach D (2018) “Effective Nearest-Neighbor Music Recommendations,” in Proceedings of the ACM Recommender Systems Challenge 2018, Vancouver BC Canada: ACM, Oct. pp. 1–6. doi: https://doi.org/10.1145/3267471.3267474

  8. Ai Q, Azizi V, Chen X, Zhang Y (Sep. 2018) Learning heterogeneous knowledge base embeddings for explainable recommendation. Algorithms 11:137. https://doi.org/10.3390/a11090137

  9. Yu Y, Wei R, Hu K, Bu Y, Zhang X (2020) “Research on an Interpretable Real-Time Information Recommendation Model based on BAS-ICF algrithm,” in 2020 Management Science Informatization and Economic Innovation Development Conference (MSIEID), Dec. pp. 304–308. doi: https://doi.org/10.1109/MSIEID52046.2020.00063

  10. Wen X (Feb. 2021) Using deep learning approach and IoT architecture to build the intelligent music recommendation system. Soft Comput 25(4):3087–3096. https://doi.org/10.1007/s00500-020-05364-y

  11. Katarya R, Verma OP (Jan. 2018) Efficient music recommender system using context graph and particle swarm. Multimedia Tools and Applications 77(2):2673–2687. https://doi.org/10.1007/s11042-017-4447-x

  12. van den Oord A, Dieleman S, Schrauwen B (2013) Deep content-based music recommendation. in Advances in neural information Processing Systems. Curran Associates, Inc. Accessed: Apr. 26, 2023. [Online].

  13. Jia B, Lv J, Liu D (2019) “Deep Learning-Based Automatic Downbeat Tracking: A Brief Review,” Multimedia Systems, vol. 25, no. 6, pp. 617–638, Dec. doi: https://doi.org/10.1007/s00530-019-00607-x

  14. Senac C, Pellegrini T, Mouret F, Pinquier J (2017) Music feature maps with convolutional neural networks for music genre classification. Jun 1–5. https://doi.org/10.1145/3095713.3095733

  15. Singh J, Sajid M, Yadav CS, Singh SS, Saini M (2022) “A Novel Deep Neural-based Music Recommendation Method considering User and Song Data,” in 6th International Conference on Trends in Electronics and Informatics (ICOEI), Apr. 2022, pp. 1–7. doi: https://doi.org/10.1109/ICOEI53556.2022.9776660

  16. Chang S-H, Abdul A, Chen J, Liao H-Y (2018) “A personalized music recommendation system using convolutional neural networks approach,” in IEEE International Conference on Applied System Invention (ICASI), Chiba: IEEE, Apr. 2018, pp. 47–49. doi: https://doi.org/10.1109/ICASI.2018.8394293

  17. Yang R, Feng L, Wang H, Yao J, Luo S (2020) Parallel recurrent convolutional neural networks-based music genre classification method for Mobile Devices. IEEE Access 8:19629–19637. https://doi.org/10.1109/ACCESS.2020.2968170

    Article  Google Scholar 

  18. Foleis JH, Tavares TF (Apr. 2020) Texture selection for automatic music genre classification. Appl Soft Comput 89:106127. https://doi.org/10.1016/j.asoc.2020.106127

  19. Scarpiniti M, Scardapane S, Comminiello D, Uncini A (2020) Music genre classification using stacked auto-encoders. 11–19. https://doi.org/10.1007/978-981-13-8950-4_2

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  1. School of Music and Dance, Hunan First Normal University, Hunan, 410002, China

    Tingrong Yin

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  1. Tingrong Yin
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Correspondence to Tingrong Yin.

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Yin, T. Music Track Recommendation Using Deep-CNN and Mel Spectrograms. Mobile Netw Appl (2023). https://doi.org/10.1007/s11036-023-02170-2

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