Analysis of the Similarity Estimation Schemes for Music and Applications

Yanjun Chen; Ning Li

doi:doi:10.11648/j.ijecs.20230806.16

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Analysis of the Similarity Estimation Schemes for Music and Applications

Yanjun Chen, Ning Li^*

Published in International Journal of Education, Culture and Society (Volume 8, Issue 6)

Received: 6 November 2023 Accepted: 28 November 2023 Published: 29 November 2023

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Abstract

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.

Published in	International Journal of Education, Culture and Society (Volume 8, Issue 6)
DOI	10.11648/j.ijecs.20230806.16
Page(s)	261-267
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Music Similarity, Music Information Retrieval, Artificial Intelligence

References

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[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.
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[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.
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[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.
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[16]	Gómez, E. Tonal description of music audio signals. PhD Thesis, Universität Pompeu Fabra, Barcelona, 2006.
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[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.
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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

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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

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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

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@article{10.11648/j.ijecs.20230806.16,
  author = {Yanjun Chen and Ning Li},
  title = {Analysis of the Similarity Estimation Schemes for Music and Applications},
  journal = {International Journal of Education, Culture and Society},
  volume = {8},
  number = {6},
  pages = {261-267},
  doi = {10.11648/j.ijecs.20230806.16},
  url = {https://doi.org/10.11648/j.ijecs.20230806.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijecs.20230806.16},
  abstract = {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.
},
 year = {2023}
}

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TY  - JOUR
T1  - Analysis of the Similarity Estimation Schemes for Music and Applications
AU  - Yanjun Chen
AU  - Ning Li
Y1  - 2023/11/29
PY  - 2023
N1  - https://doi.org/10.11648/j.ijecs.20230806.16
DO  - 10.11648/j.ijecs.20230806.16
T2  - International Journal of Education, Culture and Society
JF  - International Journal of Education, Culture and Society
JO  - International Journal of Education, Culture and Society
SP  - 261
EP  - 267
PB  - Science Publishing Group
SN  - 2575-3363
UR  - https://doi.org/10.11648/j.ijecs.20230806.16
AB  - 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.

VL  - 8
IS  - 6
ER  -

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Author Information

Yanjun Chen

Department of Music, Shenzhen University, Shenzhen, China
Ning Li

Department of Music, Shenzhen University, Shenzhen, China

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Plain Text BibTeX RIS

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

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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

Copy | Download

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

Copy | Download

@article{10.11648/j.ijecs.20230806.16,
  author = {Yanjun Chen and Ning Li},
  title = {Analysis of the Similarity Estimation Schemes for Music and Applications},
  journal = {International Journal of Education, Culture and Society},
  volume = {8},
  number = {6},
  pages = {261-267},
  doi = {10.11648/j.ijecs.20230806.16},
  url = {https://doi.org/10.11648/j.ijecs.20230806.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijecs.20230806.16},
  abstract = {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.
},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Analysis of the Similarity Estimation Schemes for Music and Applications
AU  - Yanjun Chen
AU  - Ning Li
Y1  - 2023/11/29
PY  - 2023
N1  - https://doi.org/10.11648/j.ijecs.20230806.16
DO  - 10.11648/j.ijecs.20230806.16
T2  - International Journal of Education, Culture and Society
JF  - International Journal of Education, Culture and Society
JO  - International Journal of Education, Culture and Society
SP  - 261
EP  - 267
PB  - Science Publishing Group
SN  - 2575-3363
UR  - https://doi.org/10.11648/j.ijecs.20230806.16
AB  - 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.

VL  - 8
IS  - 6
ER  -

Copy | Download