Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
The Deep Learning Revolution in MIR: The Pros and Cons, the Needs and the Challenges Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Machine Learning for Computer Music Multidisciplinary Research: A Practical Case Study

Authors : Hugo Scurto, Axel Chemla–Romeu-Santos

Published in: Perception, Representations, Image, Sound, Music

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper presents a multidisciplinary case study of practice with machine learning for computer music. It builds on the scientific study of two machine learning models respectively developed for data-driven sound synthesis and interactive exploration. It details how the learning capabilities of the two models were leveraged to design and implement a musical interface focused on embodied musical interaction. It then describes how this interface was employed and applied to the composition and performance of ægo, an improvisational piece with interactive sound and image for one performer. We discuss the outputs of our research and creation process, and expose our personal reflections and insights on transdisciplinary research opportunities framed by machine learning for computer music.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Jean-Claude Risset’s Paradoxical Practice of Interdisciplinarity: Making Inseparable Both Activities of Scientist and Musician, While Resolutely Distinguishing the Two Fields
next chapter Iterative Phase Functions on the Circle and Their Projections: Connecting Circle Maps, Waveshaping, and Phase Modulation
Footnotes
4
See these video excerpts from early rehearsals: https://​vimeo.​com/​418787133.
 
Literature
1.
Akten, M., Fiebrink, R., Grierson, M.: Deep meditations: controlled navigation of latent space. Goldsmiths University of London (2018)
2.
Assayag, G., Bloch, G., Chemilier, M., Cont, A., Dubnov, S.: OMax brothers: a dynamic topology of agents for improvization learning. In: Proceedings of the 1st ACM Workshop on Audio and Music Computing Multimedia (2006)
3.
Ballet, G., Borghesi, R., Hoffmann, P., Lévy, F.: Studio online 3.0: an internet “killer application” for remote access to IRCAM sounds and processing tools. In: Journées d’Informatique Musicale (JIM) (1999)
4.
5.
6.
7.
Cage, J.: Experimental music. In: Silence: Lectures and Writings, vol. 7, p. 12 (1961)
8.
Chowning, J.M.: The synthesis of complex audio spectra by means of frequency modulation. J. Audio Eng. Soc. 21(7), 526–534 (1973)
9.
Esling, P., Chemla-Romeu-Santos, A., Bitton, A.: Bridging audio analysis, perception and synthesis with perceptually-regularized variational timbre spaces. DAFx2018 (2018)
10.
Fiebrink, R., Caramiaux, B., Dean, R., McLean, A.: The Machine Learning Algorithm as Creative Musical Tool. Oxford University Press, Oxford (2016)
11.
Ghisi, D.: Music across music: towards a corpus-based, interactive computer-aided composition. Doctoral dissertation, Paris 6 (2017)
12.
Hamel, P., Eck, D.: Learning features from music audio with deep belief networks. In: 11th International Society for Music Information Retrieval Conference (2010)
13.
14.
Kronland-Martinet, R.: The wavelet transform for analysis, synthesis, and processing of speech and music sounds. Comput. Music J. 12(4), 11–20 (1988)CrossRef
15.
16.
Montague, S.: John Cage at seventy: an interview. Am. Music 3, 205–216 (1985)CrossRef
17.
Risset, J.C., Wessel, D.L.: Exploration of timbre by analysis and synthesis. In: The Psychology of Music, pp. 113–169. Academic Press (1999)
18.
Risset, J.-C.: Fifty years of digital sound for music. In: Proceedings of the 4th Sound and Music Computing Conference (SMC) (2007)
19.
Rodet, X., Depalle, P., Poirot, G.: Speech analysis and synthesis methods based on spectral envelopes and voiced/unvoiced functions. In: European Conference on Speech Technology (1987)
20.
Scurto, H., Bevilacqua, F., Caramiaux, B.: Perceiving agent collaborative sonic exploration in interactive reinforcement learning. In: Proceedings of the 15th Sound and Music Computing Conference (SMC) (2018)
21.
Scurto, H., Kerrebroeck, B.V., Caramiaux, B., Bevilacqua, F.: Designing deep reinforcement learning for human parameter exploration. ACM Trans. Comput.-Hum. Interact. (TOCHI) 28(1), 1–35 (2021)
22.
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2018)MATH
23.
Tanaka, A., Donnarumma, M.: The body as musical instrument. In: The Oxford Handbook of Music and the Body (2018)
24.
Warnell, G., Waytowich, N., Lawhern, V., Stone, P.: Deep TAMER: interactive agent shaping in high-dimensional state spaces. In: Thirty-Second AAAI Conference on Artificial Intelligence, April 2018
25.
Ystad, S., Aramaki, M., Kronland-Martinet, R.: Timbre from sound synthesis and high-level control perspectives. In: Siedenburg, K., Saitis, C., McAdams, S., Popper, A.N., Fay, R.R. (eds.) Timbre: Acoustics, Perception, and Cognition. SHAR, vol. 69, pp. 361–389. Springer, Cham (2019). https://​doi.​org/​10.​1007/​978-3-030-14832-4_​13 CrossRef
Metadata
Title
Machine Learning for Computer Music Multidisciplinary Research: A Practical Case Study
Authors
Hugo Scurto
Axel Chemla–Romeu-Santos
Copyright Year
2021
Book
Perception, Representations, Image, Sound, Music

Print ISBN: 978-3-030-70209-0

Electronic ISBN: 978-3-030-70210-6

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-70210-6_43