Computational music: Analysis of music forms

Jing Zhao, Kok Sheik Wong, Vishnu Monn Baskaran, Kiki Adhinugraha, David Taniar

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

With the development of computational science, many fields, including computational linguistics (sequence processing) and computational vision (image processing), have enabled various applications and automation with satisfactory results. However, the development of Computational Music Analysis (CMA) is still in its infancy. The main factor hindering the development of CMA is the complex form found in music pieces, which can be studied and analyzed in many different ways. Considering the advantages of Deep Learning (DL), this paper envisions a methodology for using DL to promote the development of Music Form Analysis (MFA). First, we review some common music forms and emphasize the significance and complexity of music forms. Next, we overview the CMA in two different processing ways, i.e., sequence-based processing and image-based processing. We then revisit the aims of CMA and propose the analysis principles that need to be satisfied for achieving the new aims during music analysis, including MFA. Subsequently, we use the fugue form as an example to verify the feasibility and potential of our envisioned methodology. The results validate the potential of using DL to obtain better MFA results. Finally, the problems and challenges of applying DL in MFA are identified and concluded into two categories, namely, the music and the non-music category, for future studies.

Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2023 - 23rd International Conference, Proceedings
EditorsOsvaldo Gervasi, Beniamino Murgante, David Taniar, Bernady O. Apduhan, Ana Cristina Braga, Chiara Garau, Anastasia Stratigea
Place of PublicationCham Switzerland
PublisherSpringer
Pages366-384
Number of pages19
ISBN (Electronic)9783031368059
ISBN (Print)9783031368042
DOIs
Publication statusPublished - 2023
EventInternational Conference on Computational Science and Its Applications 2023 - Athens, Greece
Duration: 3 Jul 20236 Jul 2023
Conference number: 23rd
https://link.springer.com/book/10.1007/978-3-031-36805-9 (Proceedings)
https://iccsa.org/the-location-of-iccsa-2023-has-been-moved-to-athens (Website)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume13956
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Computational Science and Its Applications 2023
Abbreviated titleICCSA 2023
Country/TerritoryGreece
CityAthens
Period3/07/236/07/23
Internet address

Keywords

  • Computational music analysis
  • Computational science
  • Deep learning
  • Music form

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