GenMuse: An Evolutionary Creativity Enhancement Tool

Massimo Salomoni; Jenny Carter

doi:10.1007/978-3-030-34885-4_18

GenMuse: An Evolutionary Creativity Enhancement Tool

Massimo Salomoni, Jenny Carter

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

Abstract

Creativity is often defined as the creation of something novel through the use of imagination. But for all artists, creativity is also the exploration of new and unknown areas within their specific art. Is it possible to stimulate creativity through a system that creates inspiring original music, and that is also able to learn the personal tastes of its user?
Within the project, an evolutionary approach was used in an attempt to stimulate musical creativity by supplying a composer with software that can compose short musical patterns called riffs. The software, called GenMuse, evolves populations of riffs, and makes use of a feed-forward artificial neural network to learn how to autonomously evaluate the evolved riffs to satisfy the tastes of the composer.
The results show that the approach is worthy of further investigation. The genetic algorithm produced interesting results that, according to our evaluation parameters, could be included “as is” in a musical composition, and the neural network was able to evaluate the riffs with a good success ratio.

Original language	English
Title of host publication	Artificial Intelligence XXXVI
Subtitle of host publication	39th SGAI International Conference on Artificial Intelligence, AI 2019, Cambridge, UK, December 17–19, 2019, Proceedings
Editors	Max Bramer, Miltos Petridis
Place of Publication	Cham
Publisher	Springer, Cham
Pages	229-240
Number of pages	12
Volume	LNAI 11927
ISBN (Electronic)	9783030348854
ISBN (Print)	9783030348847
DOIs	https://doi.org/10.1007/978-3-030-34885-4_18
Publication status	Published - 19 Nov 2019
Event	39th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence - Peterhouse College, Cambridge, Cambridge, United Kingdom Duration: 17 Dec 2019 → 19 Dec 2019 Conference number: 39 http://www.bcs-sgai.org/ai2019/?section=call

Publication series

Name	Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence
Publisher	Springer
Volume	LNCS 11927/LNAI 11927
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	39th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
Abbreviated title	AI 2019
Country	United Kingdom
City	Cambridge
Period	17/12/19 → 19/12/19
Internet address	http://www.bcs-sgai.org/ai2019/?section=call

Access to Document

10.1007/978-3-030-34885-4_18Licence: Other

Link to publication in Scopus

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Salomoni, M., & Carter, J. (2019). GenMuse: An Evolutionary Creativity Enhancement Tool. In M. Bramer, & M. Petridis (Eds.), Artificial Intelligence XXXVI: 39th SGAI International Conference on Artificial Intelligence, AI 2019, Cambridge, UK, December 17–19, 2019, Proceedings (Vol. LNAI 11927, pp. 229-240). (Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence; Vol. LNCS 11927/LNAI 11927). Springer, Cham. https://doi.org/10.1007/978-3-030-34885-4_18

Salomoni, Massimo ; Carter, Jenny. / GenMuse : An Evolutionary Creativity Enhancement Tool. Artificial Intelligence XXXVI: 39th SGAI International Conference on Artificial Intelligence, AI 2019, Cambridge, UK, December 17–19, 2019, Proceedings. editor / Max Bramer ; Miltos Petridis. Vol. LNAI 11927 Cham : Springer, Cham, 2019. pp. 229-240 (Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence).

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abstract = "Creativity is often defined as the creation of something novel through the use of imagination. But for all artists, creativity is also the exploration of new and unknown areas within their specific art. Is it possible to stimulate creativity through a system that creates inspiring original music, and that is also able to learn the personal tastes of its user?Within the project, an evolutionary approach was used in an attempt to stimulate musical creativity by supplying a composer with software that can compose short musical patterns called riffs. The software, called GenMuse, evolves populations of riffs, and makes use of a feed-forward artificial neural network to learn how to autonomously evaluate the evolved riffs to satisfy the tastes of the composer.The results show that the approach is worthy of further investigation. The genetic algorithm produced interesting results that, according to our evaluation parameters, could be included “as is” in a musical composition, and the neural network was able to evaluate the riffs with a good success ratio.",

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Salomoni, M & Carter, J 2019, GenMuse: An Evolutionary Creativity Enhancement Tool. in M Bramer & M Petridis (eds), Artificial Intelligence XXXVI: 39th SGAI International Conference on Artificial Intelligence, AI 2019, Cambridge, UK, December 17–19, 2019, Proceedings. vol. LNAI 11927, Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence, vol. LNCS 11927/LNAI 11927, Springer, Cham, Cham, pp. 229-240, 39th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, United Kingdom, 17/12/19. https://doi.org/10.1007/978-3-030-34885-4_18

GenMuse : An Evolutionary Creativity Enhancement Tool. / Salomoni, Massimo; Carter, Jenny.

Artificial Intelligence XXXVI: 39th SGAI International Conference on Artificial Intelligence, AI 2019, Cambridge, UK, December 17–19, 2019, Proceedings. ed. / Max Bramer; Miltos Petridis. Vol. LNAI 11927 Cham : Springer, Cham, 2019. p. 229-240 (Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence; Vol. LNCS 11927/LNAI 11927).

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

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Salomoni M, Carter J. GenMuse: An Evolutionary Creativity Enhancement Tool. In Bramer M, Petridis M, editors, Artificial Intelligence XXXVI: 39th SGAI International Conference on Artificial Intelligence, AI 2019, Cambridge, UK, December 17–19, 2019, Proceedings. Vol. LNAI 11927. Cham: Springer, Cham. 2019. p. 229-240. (Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence). https://doi.org/10.1007/978-3-030-34885-4_18