Using autonomous agents to improvise music compositions in Real-Time

Patrick Hutchings; Jon McCormack

doi:10.1007/978-3-319-55750-2_8

Using autonomous agents to improvise music compositions in Real-Time

Patrick Hutchings, Jon McCormack

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

9 Citations (Scopus)

Abstract

This paper outlines an approach to real-time music generation using melody and harmony focused agents in a process inspired by jazz improvisation. A harmony agent employs a Long Short-Term Memory (LSTM) artificial neural network trained on the chord progressions of 2986 jazz ‘standard’ compositions using a network structure novel to chord sequence analysis. The melody agent uses a rule-based system of manipulating provided, pre-composed melodies to improvise new themes and variations. The agents take turns in leading the direction of the composition based on a rating system that rewards harmonic consistency and melodic flow. In developing the multi-agent system it was found that implementing embedded spaces in the LSTM encoding process resulted in significant improvements to chord sequence learning.

Original language	English
Title of host publication	Computational Intelligence in Music, Sound, Art and Design
Subtitle of host publication	6th International Conference, EvoMUSART 2017, Proceedings
Editors	Joao Correia, Vic Ciesielski, Antonios Liapis
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	114-127
Number of pages	14
Volume	10198
ISBN (Electronic)	9783319557502
ISBN (Print)	9783319557496
DOIs	https://doi.org/10.1007/978-3-319-55750-2_8
Publication status	Published - 2017
Event	International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design 2017 - Amsterdam, Netherlands Duration: 19 Apr 2017 → 21 Apr 2017 Conference number: 6th https://link.springer.com/book/10.1007/978-3-319-55750-2 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	10198
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design 2017
Abbreviated title	EvoMUSART 2017
Country/Territory	Netherlands
City	Amsterdam
Period	19/04/17 → 21/04/17
Internet address	https://link.springer.com/book/10.1007/978-3-319-55750-2 (Proceedings)

Keywords

Artificial neural networks
Multi-agent systems
Music composition

Access to Document

10.1007/978-3-319-55750-2_8

Cite this

Hutchings, P., & McCormack, J. (2017). Using autonomous agents to improvise music compositions in Real-Time. In J. Correia, V. Ciesielski, & A. Liapis (Eds.), Computational Intelligence in Music, Sound, Art and Design : 6th International Conference, EvoMUSART 2017, Proceedings (Vol. 10198 , pp. 114-127). (Lecture Notes in Computer Science; Vol. 10198). Springer. https://doi.org/10.1007/978-3-319-55750-2_8

Hutchings, Patrick ; McCormack, Jon. / Using autonomous agents to improvise music compositions in Real-Time. Computational Intelligence in Music, Sound, Art and Design : 6th International Conference, EvoMUSART 2017, Proceedings. editor / Joao Correia ; Vic Ciesielski ; Antonios Liapis . Vol. 10198 Cham Switzerland : Springer, 2017. pp. 114-127 (Lecture Notes in Computer Science).

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abstract = "This paper outlines an approach to real-time music generation using melody and harmony focused agents in a process inspired by jazz improvisation. A harmony agent employs a Long Short-Term Memory (LSTM) artificial neural network trained on the chord progressions of 2986 jazz {\textquoteleft}standard{\textquoteright} compositions using a network structure novel to chord sequence analysis. The melody agent uses a rule-based system of manipulating provided, pre-composed melodies to improvise new themes and variations. The agents take turns in leading the direction of the composition based on a rating system that rewards harmonic consistency and melodic flow. In developing the multi-agent system it was found that implementing embedded spaces in the LSTM encoding process resulted in significant improvements to chord sequence learning.",

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note = "International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design 2017, EvoMUSART 2017 ; Conference date: 19-04-2017 Through 21-04-2017",

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Hutchings, P & McCormack, J 2017, Using autonomous agents to improvise music compositions in Real-Time. in J Correia, V Ciesielski & A Liapis (eds), Computational Intelligence in Music, Sound, Art and Design : 6th International Conference, EvoMUSART 2017, Proceedings. vol. 10198 , Lecture Notes in Computer Science, vol. 10198, Springer, Cham Switzerland, pp. 114-127, International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design 2017, Amsterdam, Netherlands, 19/04/17. https://doi.org/10.1007/978-3-319-55750-2_8

Using autonomous agents to improvise music compositions in Real-Time. / Hutchings, Patrick; McCormack, Jon.

Computational Intelligence in Music, Sound, Art and Design : 6th International Conference, EvoMUSART 2017, Proceedings. ed. / Joao Correia; Vic Ciesielski; Antonios Liapis . Vol. 10198 Cham Switzerland : Springer, 2017. p. 114-127 (Lecture Notes in Computer Science; Vol. 10198).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AB - This paper outlines an approach to real-time music generation using melody and harmony focused agents in a process inspired by jazz improvisation. A harmony agent employs a Long Short-Term Memory (LSTM) artificial neural network trained on the chord progressions of 2986 jazz ‘standard’ compositions using a network structure novel to chord sequence analysis. The melody agent uses a rule-based system of manipulating provided, pre-composed melodies to improvise new themes and variations. The agents take turns in leading the direction of the composition based on a rating system that rewards harmonic consistency and melodic flow. In developing the multi-agent system it was found that implementing embedded spaces in the LSTM encoding process resulted in significant improvements to chord sequence learning.

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Hutchings P, McCormack J. Using autonomous agents to improvise music compositions in Real-Time. In Correia J, Ciesielski V, Liapis A, editors, Computational Intelligence in Music, Sound, Art and Design : 6th International Conference, EvoMUSART 2017, Proceedings. Vol. 10198 . Cham Switzerland: Springer. 2017. p. 114-127. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-55750-2_8

Using autonomous agents to improvise music compositions in Real-Time

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Keywords

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