International Journal of Education, Culture and Society

Submit a Manuscript

Publishing with us to make your research visible to the widest possible audience.

Propose a Special Issue

Building a community of authors and readers to discuss the latest research and develop new ideas.

Research Article |

Analysis of the Similarity Estimation Schemes for Music and Applications

With the maturation of big data technology and artificial intelligence algorithms within music information retrieval, this research delves into the nuanced landscape of music similarity computation and evaluation methods, along with their multifaceted applications. Positioned within the broader music information retrieval domain, the study addresses pivotal challenges utilizing advanced technologies. Central to the investigation is the exploration of music similarity detection, a vital facet of music information retrieval crucial for tasks like music plagiarism identification, song classification, and the development of music recommendation systems. The study meticulously introduces various applications of similarity computation and meticulously dissects the principles and processes of music feature extraction, incorporating methodologies such as mel-frequency cepstral coefficients, harmonic pitch class profile, convolutional neural networks, and recurrent neural networks. A comprehensive survey of prevailing models and approaches for computing similarity is presented. Beyond conventional measures like cosine and Euclidean distances, the research scrutinizes the integration of artificial intelligence algorithms and models, notably support vector machines, into the computation of music similarity. The study meticulously outlines the advantages and limitations of these methodologies, offering nuanced insights. These findings serve as a valuable reference for researchers aiming to comprehend the intricacies of music similarity computation, providing a foundation for refining existing models. The study accentuates the synergy between big data, artificial intelligence, and music information retrieval, envisaging a landscape where these technologies collectively propel the field forward.

Music Similarity, Music Information Retrieval, Artificial Intelligence

APA Style

Chen, Y., Li, N. (2023). Analysis of the Similarity Estimation Schemes for Music and Applications. International Journal of Education, Culture and Society, 8(6), 261-267. https://doi.org/10.11648/j.ijecs.20230806.16

ACS Style

Chen, Y.; Li, N. Analysis of the Similarity Estimation Schemes for Music and Applications. Int. J. Educ. Cult. Soc. 2023, 8(6), 261-267. doi: 10.11648/j.ijecs.20230806.16

AMA Style

Chen Y, Li N. Analysis of the Similarity Estimation Schemes for Music and Applications. Int J Educ Cult Soc. 2023;8(6):261-267. doi: 10.11648/j.ijecs.20230806.16

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Funk, T., A Musical Suite Composed by an Electronic Brain: Reexamining the Illiac Suite and the Legacy of Lejaren A. Hiller Jr., Leonardo Music Journal, 28, 2018, pp. 19-24.
2. Meng Tongtong, Development and Application Analysis of Computer Music, Music Space and Time, (8x), 2018, pp. 74.
3. Cheng, C., Comparative Research on Song Similarity Based on Deep Learning, Doctoral Dissertation, Beijing: Beijing University of Posts and Telecommunications, 2020.
4. Li, W., Li, Z., & Gao, Y. Understanding digital music: A review of music information retrieval techniques, Fudan Journal (Natural Science Edition), 57(3), 2018, pp. 271-313.
5. Chen, N., Li, W., & Xiao, H. Fusing similarity functions for cover song identification, Multimedia Tools and Applications, 77, 2018, pp. 2629-2652.
6. Seyerlehner, K., Widmer, G., & Pohle, T. Fusing block-level features for music similarity estimation. In Proc. of the 13th Int. Conference on Digital Audio Effects (DAFx-10), 2010, pp. 225-232.
7. Flexer, A., & Grill, T. The problem of limited inter-rater agreement in modelling music similarity, Journal of new music research, 45(3), 2016, pp. 239-251.
8. Knees, P., Schedl, M., Knees, P., & Schedl, M. (2016). Introduction to music similarity and retrieval. Music Similarity and Retrieval, 1-30.
9. Berenzweig, A., Logan, B., Ellis, D. P., & Whitman, B. A large-scale evaluation of acoustic and subjective music-similarity measures, Computer Music Journal, 2004, pp. 63-76.
10. Knees, P., & Schedl, M. A survey of music similarity and recommendation from music context data, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 10(1), 2013, pp. 1-21.
11. Chen, N., Li, M., & Xiao, H. Two-layer similarity fusion model for cover song identification, EURASIP Journal on Audio, Speech, and Music Processing, 2017 (1), 2017, 1-15.
12. Seyerlehner, K., Schedl, M., Pohle, T., & Knees, P. Using block-level features for genre classification, tag classification and music similarity estimation, Submission to Audio Music Similarity and Retrieval Task of MIREX, 2010 (2), 2010, pp. 3.
13. Fujishima, T. Realtime Chord Recognition of Musical Sound: a System Using Common Lisp Music. In Proc. of the International Computer Music Conference 1999, 1999, pp. 464-467.
14. Serra, J., Serra, X., & Andrzejak, R. G. Cross recurrence quantification for cover song identification. New Journal of Physics, 11(9), 2009, 093017.
15. Chen, N., Downie, J. S., Xiao, H. D., & Zhu, Y. Cochlear pitch class profile for cover song identification, Applied Acoustics, 99, 2015, pp. 92-96.
16. Gómez, E. Tonal description of music audio signals. PhD Thesis, Universität Pompeu Fabra, Barcelona, 2006.
17. Chen, N., & Xiao, H. D. Similarity fusion scheme for cover song identification. Electronics Letters, 52(13), 2016, pp. 1173-1175.
18. Yu, Z., Xu, X., Chen, X., & Yang, D. Temporal Pyramid Pooling Convolutional Neural Network for Cover Song Identification. In Proc. of the International Joint Conference on Artificial Intelligence 2019, 2019, pp. 4846-4852.
19. Choi, K., Hawthorne, C., Simon, I., Dinculescu, M., & Engel, J. Encoding musical style with transformer autoencoders. In Proc. of the International Conference on Machine Learning, 2020, pp. 1899-1908.
20. Shakirova, E. Collaborative filtering for music recommender system. In Proc. of the 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 2017, pp. 548-550.
21. Schedl, M. Deep learning in music recommendation systems. Frontiers in Applied Mathematics and Statistics, 2019, pp. 44.
22. Knees, P., & Schedl, M. Music similarity and retrieval: an introduction to audio-and web-based strategies, Vol. 36. Heidelberg: Springer, 2016.
23. Murthy, Y. S., & Koolagudi, S. G. Content-based music information retrieval (cb-mir) and its applications toward the music industry: A review. ACM Computing Surveys (CSUR), 51(3), 2018, pp. 1-46.
24. Ndou, N., Ajoodha, R., & Jadhav, A. Music genre classification: A review of deep-learning and traditional machine-learning approaches. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), 2021, pp. 1-6.
25. Herremans, D., Chuan, C. H., & Chew, E. A functional taxonomy of music generation systems. ACM Computing Surveys (CSUR), 50(5), 2017, pp. 1-30.