Skip to main content
Springer Nature Link
Log in

Computational Music: Analysis of Music Forms

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2023 (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13956 ))

Included in the following conference series:

  • 1146 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The mismatch in lengths for S and CS would not affect the results on the detected occurrences.

References

  1. Abiodun, O.I., Jantan, A., Omolara, A.E., Dada, K.V., Mohamed, N.A., Arshad, H.: State-of-the-art in artificial neural network applications: a survey. Heliyon 4(11), e00938 (2018)

    Article  Google Scholar 

  2. Allegraud, P., et al.: Learning sonata form structure on mozart’s string quartets. Trans. Int. Society Music Inform. Retrieval (TISMIR) 2(1), 82–96 (2019)

    Article  Google Scholar 

  3. Anagnostopoulou, C., Buteau, C.: Can computational music analysis be both musical and computational? J. Math. Music 4(2), 75–83 (2010)

    Google Scholar 

  4. Arnold, J.M.: The role of chromaticism in Chopin’s sonata forms: a Schenkerian view. Northwestern University (1992)

    Google Scholar 

  5. Basiri, M.E., Nemati, S., Abdar, M., Cambria, E., Acharya, U.R.: Abcdm: an attention-based bidirectional cnn-rnn deep model for sentiment analysis. Futur. Gener. Comput. Syst. 115, 279–294 (2021)

    Article  Google Scholar 

  6. Bergstrom, T., Karahalios, K., Hart, J.C.: Isochords: visualizing structure in music. In: Proceedings of Graphics Interface 2007, pp. 297–304 (2007)

    Google Scholar 

  7. Bigo, L., Giraud, M., Groult, R., Guiomard-Kagan, N., Levé, F.: Sketching sonata form structure in selected classical string quartets. In: ISMIR 2017-International Society for Music Information Retrieval Conference (2017)

    Google Scholar 

  8. Buccoli, M., Zanoni, M., Sarti, A., Tubaro, S., Andreoletti, D.: Unsupervised feature learning for music structural analysis. In: 2016 24th European Signal Processing Conference (EUSIPCO), pp. 993–997. IEEE (2016)

    Google Scholar 

  9. Buisson, M., Mcfee, B., Essid, S., Crayencour, H.C.: Learning multi-level representations for hierarchical music structure analysis. In: International Society for Music Information Retrieval (ISMIR) (2022)

    Google Scholar 

  10. Burgoyne, J.A., Fujinaga, I., Downie, J.S.: Music information retrieval. A new companion to digital humanities, pp. 213–228 (2015)

    Google Scholar 

  11. Carr, C., Odell-Miller, H., Priebe, S.: A systematic review of music therapy practice and outcomes with acute adult psychiatric in-patients. PLoS ONE 8(8), e70252 (2013)

    Article  Google Scholar 

  12. Chan, W.Y., Qu, H., Mak, W.H.: Visualizing the semantic structure in classical music works. IEEE Trans. Visual Comput. Graphics 16(1), 161–173 (2009)

    Article  Google Scholar 

  13. Chawin, D., Rom, U.B.: Sliding-window pitch-class histograms as a means of modeling musical form. Trans. Int. Society for Music Inform. Retrieval 4(1), (2021)

    Google Scholar 

  14. Chen, P., Zhao, L., Xin, Z., Qiang, Y., Zhang, M., Li, T.: A scheme of midi music emotion classification based on fuzzy theme extraction and neural network. In: 2016 12th International Conference on Computational Intelligence and Security (CIS), pp. 323–326. IEEE (2016)

    Google Scholar 

  15. Cheng, T., Smith, J.B., Goto, M.: Music structure boundary detection and labelling by a deconvolution of path-enhanced self-similarity matrix. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 106–110. IEEE (2018)

    Google Scholar 

  16. Chew, E.: Cosmos: Computational shaping and modeling of musical structures. Front. Psychol. 13 (2022). https://doi.org/10.3389/fpsyg.2022.527539

  17. Chillara, S., Kavitha, A., Neginhal, S.A., Haldia, S., Vidyullatha, K.: Music genre classification using machine learning algorithms: a comparison. Int. Res. J. Eng. Technol. 6(5), 851–858 (2019)

    Google Scholar 

  18. Clercq, T.d.: Embracing ambiguity in the analysis of form in pop/rock music, 1982–1991. Music Theory Online 23(3), (2017)

    Google Scholar 

  19. Corazza, G.E., Agnoli, S., Martello, S.: Counterpoint as a principle of creativity: extracting divergent modifiers from’the art of fugue’by johann sebastian bach. Musica Docta 4, 93–105 (2014)

    Google Scholar 

  20. Dai, S., Jin, Z., Gomes, C., Dannenberg, R.B.: Controllable deep melody generation via hierarchical music structure representation. arXiv preprint arXiv:2109.00663 (2021)

  21. De Prisco, R., et al: Music plagiarism at a glance: metrics of similarity and visualizations. In: 2017 21st International Conference Information Visualisation (IV), pp. 410–415. IEEE (2017)

    Google Scholar 

  22. De Prisco, R., Malandrino, D., Pirozzi, D., Zaccagnino, G., Zaccagnino, R.: Understanding the structure of musical compositions: is visualization an effective approach? Inf. Vis. 16(2), 139–152 (2017)

    Article  Google Scholar 

  23. Dent, E.J.: Binary and ternary form. Music Lett. 17(4), 309–321 (1936)

    Article  Google Scholar 

  24. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  25. Devlin, K., Alshaikh, J.T., Pantelyat, A.: Music therapy and music-based interventions for movement disorders. Curr. Neurol. Neurosci. Rep. 19, 1–13 (2019)

    Article  Google Scholar 

  26. Dirst, M., Weigend, A.S.: On completing js bach’s last fugue. Time Series Prediction: Forecasting the Future and Understanding the Past, pp. 151–177 (1994)

    Google Scholar 

  27. Fuentes, M., McFee, B., Crayencour, H.C., Essid, S., Bello, J.P.: A music structure informed downbeat tracking system using skip-chain conditional random fields and deep learning. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 481–485. IEEE (2019)

    Google Scholar 

  28. Giraud, M., Groult, R., Leguy, E., Levé, F.: Computational fugue analysis. Comput. Music. J. 39(2), 77–96 (2015)

    Article  Google Scholar 

  29. Giraud, M., Groult, R., Levé, F.: Subject and counter-subject detection for analysis of the well-tempered clavier fugues. In: Aramaki, M., Barthet, M., Kronland-Martinet, R., Ystad, S. (eds.) CMMR 2012. LNCS, vol. 7900, pp. 422–438. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41248-6_24

    Chapter  Google Scholar 

  30. Hernandez-Olivan, C., Beltran, J.R., Diaz-Guerra, D.: Music boundary detection using convolutional neural networks: a comparative analysis of combined input features. Int. J. Interact. Multimedia Artif. Intell. 7(2), 78 (2021). https://doi.org/10.9781/ijimai.2021.10.005

    Article  Google Scholar 

  31. Huang, C.Z.A., Cooijmans, T., Roberts, A., Courville, A., Eck, D.: Counterpoint by convolution. arXiv preprint arXiv:1903.07227 (2019)

  32. Jackendoff, R., Lerdahl, F.: The capacity for music: what is it, and what’s special about it? Cognition 100(1), 33–72 (2006)

    Article  Google Scholar 

  33. Jain, A., Zamir, A.R., Savarese, S., Saxena, A.: Structural-rnn: Deep learning on spatio-temporal graphs. In: Proceedings of The IEEE Conference On Computer Vision And Pattern Recognition, pp. 5308–5317 (2016)

    Google Scholar 

  34. Jin, C., Tie, Y., Bai, Y., Lv, X., Liu, S.: A style-specific music composition neural network. Neural Process. Lett. 52, 1893–1912 (2020)

    Article  Google Scholar 

  35. Jun, S., Hwang, E.: Music segmentation and summarization based on self-similarity matrix. In: Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication, p. 4. No. 82 in ICUIMC ’13, Association for Computing Machinery, New York, NY, USA (2013)

    Google Scholar 

  36. Kao, W.T., Lee, H.Y.: Is bert a cross-disciplinary knowledge learner? a surprising finding of pre-trained models’ transferability. arXiv preprint arXiv:2103.07162 (2021)

  37. Kenner, J., Baker, F.A., Treloyn, S.: Perspectives on musical competence for people with borderline personality disorder in group music therapy. Nord. J. Music. Ther. 29(3), 271–287 (2020)

    Article  Google Scholar 

  38. Kumar, C., Dutta, S., Chakborty, S.: Musical cryptography using genetic algorithm. In: 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014], pp. 1742–1747. IEEE (2014)

    Google Scholar 

  39. Lawes, M.: Creating a covid-19 guided imagery and music (gim) self-help resource for those with mild to moderate symptoms of the disease. Approaches: An Interdisciplinary Journal of Music Therapy, pp. 1–17 (2020)

    Google Scholar 

  40. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  41. Lewin, D.: Notes on the opening of the f# minor fugue from wtci. J. Music Theor. 42(2), 235–239 (1998)

    Article  Google Scholar 

  42. Manning, C.D.: Computational linguistics and deep learning. Comput. Linguist. 41(4), 701–707 (2015)

    Article  MathSciNet  Google Scholar 

  43. Marandi, Y.M.H., Sajedi, H., Pirasteh, S.: A novel method to musicalize shape and visualize music and a novel technique in music cryptography. Multimedia Tools Appl. 80, 7451–7477 (2021)

    Article  Google Scholar 

  44. Marr, D.: Vision: A computational investigation into the human representation and processing of visual information. MIT press (2010)

    Google Scholar 

  45. Marsden, Alan: Music analysis by computer: ontology and epistemology. In: Computational Music Analysis, pp. 3–28. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-25931-4_1

    Chapter  MATH  Google Scholar 

  46. Mauch, M., Levy, M.: Structural change on multiple time scales as a correlate of musical complexity, pp. 489–494 (01 2011)

    Google Scholar 

  47. Meredith, D.: Music analysis and point-set compression. J. New Music Res. 44(3), 245–270 (2015)

    Article  Google Scholar 

  48. Miller, R.I.M.: Unity and contrast: A study of Ludwig van Beethoven’s use of variation form in his symphonies, string quartets and piano sonatas. University of Glasgow (United Kingdom) (2003)

    Google Scholar 

  49. Müller, M.: Music Structure Analysis, pp. 167–236. Springer International Publishing, Cham (2015)

    Google Scholar 

  50. North, A.C., Hargreaves, D.J., Hargreaves, J.J.: Uses of music in everyday life. Music. Percept. 22(1), 41–77 (2004)

    Article  Google Scholar 

  51. Panda, R., Malheiro, R.M., Paiva, R.P.: Audio features for music emotion recognition: a survey. IEEE Trans. Affective Comput, 99, 1–1 (2020)

    Google Scholar 

  52. Pang, T.H.: The variation technique in selected piano works of Haydn, Mozart, Beethoven and Schubert: A performance project. University of Maryland, College Park (1998)

    Google Scholar 

  53. Paulus, J., Müller, M., Klapuri, A.: Audio-based music structure analysis. In: Proceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR 2010, pp. 625–636 (01 2010)

    Google Scholar 

  54. Pereira, R.M., Costa, Y.M., Aguiar, R.L., Britto, A.S., Oliveira, L.E., Silla, C.N.: Representation learning vs. handcrafted features for music genre classification. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2019)

    Google Scholar 

  55. Pipalia, K., Bhadja, R., Shukla, M.: Comparative analysis of different transformer based architectures used in sentiment analysis. In: 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART), pp. 411–415. IEEE (2020)

    Google Scholar 

  56. Prout, E.: Fugue. Library Reprints (1891)

    Google Scholar 

  57. Ratner, L.: Harmonic aspects of classic form. J. Am. Musicol. Soc. 2(3), 159–168 (1949)

    Article  Google Scholar 

  58. Roy, S., Biswas, M., De, D.: imusic: a session-sensitive clustered classical music recommender system using contextual representation learning. Multimedia Tools Appl. 79, 24119–24155 (2020)

    Article  Google Scholar 

  59. Sheldon, D.A.: The stretto principle: some thoughts on fugue as form. J. Musicol. 8(4), 553–568 (1990)

    Article  Google Scholar 

  60. Shi, E.R., Zhang, Q.: A domain-general perspective on the role of the basal ganglia in language and music: Benefits of music therapy for the treatment of aphasia. Brain Lang. 206, 104811 (2020)

    Article  Google Scholar 

  61. Sutton, E.: Virginia Woolf and Classical Music: Politics, Aesthetics. Edinburgh University Press, Form (2013)

    Book  Google Scholar 

  62. Tavares, J.M.R., Jorge, R.M.N., et al.: Topics in Medical Image Processing and Computational Vision. Springer (2013). https://doi.org/10.1007/978-94-007-0726-9

  63. Umer, S., Mondal, R., Pandey, H.M., Rout, R.K.: Deep features based convolutional neural network model for text and non-text region segmentation from document images. Appl. Soft Comput. 113, 107917 (2021)

    Article  Google Scholar 

  64. Verma, P.K., Agrawal, P., Madaan, V., Prodan, R.: Mcred: multi-modal message credibility for fake news detection using bert and cnn. Journal of Ambient Intelligence and Humanized Computing, pp. 1–13 (2022). DOI: https://doi.org/10.1007/s12652-022-04338-2

  65. Wang, W., et al.: Internimage: Exploring large-scale vision foundation models with deformable convolutions. arXiv preprint arXiv:2211.05778 (2022)

  66. Webster, J.: Schubert’s sonata form and brahms’s first maturity. Nineteenth-Century Music, pp. 18–35 (1978)

    Google Scholar 

  67. Wen, R., Chen, K., Xu, K., Zhang, Y., Wu, J.: Music main melody extraction by an interval pattern recognition algorithm. In: 2019 Chinese Control Conference (CCC), pp. 7728–7733. IEEE (2019)

    Google Scholar 

  68. Wu, J., Liu, X., Hu, X., Zhu, J.: Popmnet: generating structured pop music melodies using neural networks. Artif. Intell. 286, 103303 (2020)

    Article  Google Scholar 

  69. Wu, X., Lv, S., Zang, L., Han, J., Hu, S.: Conditional BERT contextual augmentation. In: Rodrigues, J.M.F., et al. (eds.) ICCS 2019. LNCS, vol. 11539, pp. 84–95. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22747-0_7

  70. Young, J.O.: How classical music is better than popular music. Philosophy 91(4), 523–540 (2016)

    Article  Google Scholar 

  71. Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1015–1022. IEEE (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Monash University Malaysia, Bandar Sunway, SL, 47500, Malaysia

    Jing Zhao, KokSheik Wong & Vishnu Monn Baskaran

  2. Monash University, Clayton, VIC, 3800, Australia

    David Taniar

  3. La Trobe University, Bundoora, VIC, 3086, Australia

    Kiki Adhinugraha

Authors
  1. Jing Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. KokSheik Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vishnu Monn Baskaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kiki Adhinugraha
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David Taniar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to KokSheik Wong .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Monash University, Clayton, VIC, Australia

    David Taniar

  4. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  5. University of Minho, Braga, Portugal

    Ana Cristina Braga

  6. University of Cagliari, Cagliari, Italy

    Chiara Garau

  7. National Technical University of Athens, Athens, Greece

    Anastasia Stratigea

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhao, J., Wong, K., Baskaran, V.M., Adhinugraha, K., Taniar, D. (2023). Computational Music: Analysis of Music Forms. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, vol 13956 . Springer, Cham. https://doi.org/10.1007/978-3-031-36805-9_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-36805-9_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36804-2

  • Online ISBN: 978-3-031-36805-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics