Article

Evolving musical performance profiles using genetic algorithms with structural fitness

Authors:

Qijun Zhang,

Eduardo Reck MirandaAuthors Info & Claims

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

Pages 1833 - 1840

https://doi.org/10.1145/1143997.1144292

Published: 08 July 2006 Publication History

Get Access

Abstract

This paper presents a system that uses Genetic Algorithm (GA) to evolve hierarchical pulse sets (i.e., hierarchical duration vs. amplitude matrices) for expressive music performance by machines. The performance profile for a piece of music is represented using pulse sets and the fitness (for the GA) is derived from the structure of the piece to be performed; hence the term "structural fitness". Randomly initiated pulse sets are selected and evolved using GA. The fitness value is calculated by measuring the pulse set's ability of highlighting musical structures. This measurement is based upon generative rules for expressive music performance. This is the first stage of a project, which is aimed at the design of a dynamic model for the evolution of expressive performance profiles by interacting agents in an artificial society of musicians and listeners.

References

[1]

Blickle, T. "A comparison of selection schemes used in genetic algorithms". Technical report, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH) Zurich (1995).

Google Scholar

[2]

Clarke, E. F. "Generative Processes in Music". The Psychology of Performance, Improvisation, and Composition. Oxford Science Publications (1988).

Google Scholar

[3]

Clynes, M. "Generative principles of musical thought integration of microstructure with structure". Journal For The Integrated Study Of Artificial Intelligence, Cognitive Science And Applied Epistemology 3 (1986) 185--223.

Google Scholar

[4]

Clynes, M. "Microstructural musical linguistics: composers' pulses are liked most by the best musicians". Cognition. International Journal of Cognitive Science, 55, (1995), 269--310.

Crossref

Google Scholar

[5]

Davidson, J. W. and North, A. C. The Social Psychology of Music. Oxford University Press (2006).

Google Scholar

[6]

Oosten, P. van "Critical study of Sundberg's rules for expression in the performance of melodies". Contemporary Music Review, 1993, Vol.9, 267--274

Crossref

Google Scholar

[7]

Poli, G. D. "Methodologies for expressiveness modelling of and for music performance". Journal Of New Music Research 33 (2004) 189--202.

Crossref

Google Scholar

[8]

Repp, B. H. "Diversity and commonality in music performance: An analysis of timing microstructure in Schumann's Traumerei". Journal of the Acoustical Society of America (92).

Google Scholar

[9]

Temperly, D. The Cognition of Basic Musical Structures. The Mit Press (2004).

Digital Library

Google Scholar

[10]

Widmer, G. and Goebl, W. "Computational models of expressive music performance: The state of the art". Journal of New Music Research 33 (2004) 203--216.

Crossref

Google Scholar

Cited By

View all

Sabitha RMajji SKathiravan MKumar SKharade KKaranam S(2021)Artificial Intelligence Based Music Composition System-Multi Algorithmic Music Arranger(MAGMA)2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC)10.1109/ICESC51422.2021.9532706(1808-1813)Online publication date: 4-Aug-2021
https://doi.org/10.1109/ICESC51422.2021.9532706
Kirke AMiranda E(2021)Performance Creativity in Computer Systems for Expressive Performance of MusicHandbook of Artificial Intelligence for Music10.1007/978-3-030-72116-9_19(521-584)Online publication date: 3-Jul-2021
https://doi.org/10.1007/978-3-030-72116-9_19
de Mantaras RArcos J(2012)2011 Robert S. Engelmore Memorial Lecture AwardAI Magazine10.1609/aimag.v33i4.240533:4(22-32)Online publication date: 1-Dec-2012
https://dl.acm.org/doi/10.1609/aimag.v33i4.2405
Show More Cited By

Index Terms

Evolving musical performance profiles using genetic algorithms with structural fitness
1. Applied computing
  1. Arts and humanities
    1. Performing arts
2. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Cognitive robotics
    2. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science

Recommendations

Genetic algorithms and the abc music notation language for rock music composition
GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

In this paper a music composition system based on genetic algorithms (GAs) will be presented. It can create multi-instrumental, guitar-orientated rock music using objective measures for its fitness functions. The output of this system is a song in the ...
Evolving four-part harmony using genetic algorithms
EvoApplications'11: Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II

This paper presents a genetic algorithm that evolves a fourpart musical composition-melodically, harmonically, and rhythmically. Unlike similar attempts in the literature, our composition evolves from a single musical chord without human intervention or ...
Understanding expressive music performance using genetic algorithms
EC'05: Proceedings of the 3rd European conference on Applications of Evolutionary Computing

In this paper, we describe an approach to learning expressive performance rules from monophonic Jazz standards recordings by a skilled saxophonist. We use a melodic transcription system which extracts a set of acoustic features from the recordings ...

Comments

Information & Contributors

Information

Published In

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

July 2006

2004 pages

ISBN:1595931864

DOI:10.1145/1143997

General Chair:
Mike Cattolico
Tiger Mountain Scientific, Inc., USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2006

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

GECCO06

Sponsor:

GECCO06: Genetic and Evolutionary Computation Conference

July 8 - 12, 2006

Washington, Seattle, USA

Acceptance Rates

GECCO '06 Paper Acceptance Rate 205 of 446 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
25
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Sabitha RMajji SKathiravan MKumar SKharade KKaranam S(2021)Artificial Intelligence Based Music Composition System-Multi Algorithmic Music Arranger(MAGMA)2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC)10.1109/ICESC51422.2021.9532706(1808-1813)Online publication date: 4-Aug-2021
https://doi.org/10.1109/ICESC51422.2021.9532706
Kirke AMiranda E(2021)Performance Creativity in Computer Systems for Expressive Performance of MusicHandbook of Artificial Intelligence for Music10.1007/978-3-030-72116-9_19(521-584)Online publication date: 3-Jul-2021
https://doi.org/10.1007/978-3-030-72116-9_19
de Mantaras RArcos J(2012)2011 Robert S. Engelmore Memorial Lecture AwardAI Magazine10.1609/aimag.v33i4.240533:4(22-32)Online publication date: 1-Dec-2012
https://dl.acm.org/doi/10.1609/aimag.v33i4.2405
Miranda EKirke AZhang Q(2012)Artificial Evolution of Expressive Performance of Music: An Imitative Multi-Agent Systems ApproachGuide to Computing for Expressive Music Performance10.1007/978-1-4471-4123-5_4(99-121)Online publication date: 31-May-2012
https://doi.org/10.1007/978-1-4471-4123-5_4
Miranda EKirke AZhang Q(2010)Artificial evolution of expressive performance of musicComputer Music Journal10.1162/comj.2010.34.1.8034:1(80-96)Online publication date: 1-Mar-2010
https://dl.acm.org/doi/10.1162/comj.2010.34.1.80
Kirke AMiranda E(2009)A survey of computer systems for expressive music performanceACM Computing Surveys10.1145/1592451.159245442:1(1-41)Online publication date: 14-Dec-2009
https://dl.acm.org/doi/10.1145/1592451.1592454
Zhang QMiranda E(2006)Towards an Evolution Model of Expressive Music PerformanceSixth International Conference on Intelligent Systems Design and Applications10.1109/ISDA.2006.253781(1189-1194)Online publication date: Oct-2006
https://doi.org/10.1109/ISDA.2006.253781

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Genetic algorithms and the abc music notation language for rock music composition

Evolving four-part harmony using genetic algorithms

Understanding expressive music performance using genetic algorithms

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations