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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.

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 ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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