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Inducing a generative expressive performance model using a sequential-covering genetic algorithm

Published: 07 July 2007 Publication History

Abstract

In this paper, we describe an evolutionary approach to inducing a generative model of expressive music performance for Jazz saxophone. We begin with a collection of audio recordings of real Jazz saxophone performances from which we extract a symbolic representation of the musician's expressive performance. We then apply an evolutionary algorithm to the symbolic representation in order to obtain computational models for different aspects of expressive performance. Finally, we use these models to automatically synthesize performances with the expressiveness that characterizes the music generated by a professional saxophonist.

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Cited By

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  • (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
  • (2019)Related Work and a Taxonomy of Musical Intelligence TasksSequential Decision-Making in Musical Intelligence10.1007/978-3-030-30519-2_8(143-196)Online publication date: 2-Oct-2019
  • (2012)An Overview of Computer Systems for Expressive Music PerformanceGuide to Computing for Expressive Music Performance10.1007/978-1-4471-4123-5_1(1-47)Online publication date: 31-May-2012
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    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958
    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]

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    Published: 07 July 2007

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    Author Tags

    1. expressive music performance
    2. genetic algorithms

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    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    View all
    • (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
    • (2019)Related Work and a Taxonomy of Musical Intelligence TasksSequential Decision-Making in Musical Intelligence10.1007/978-3-030-30519-2_8(143-196)Online publication date: 2-Oct-2019
    • (2012)An Overview of Computer Systems for Expressive Music PerformanceGuide to Computing for Expressive Music Performance10.1007/978-1-4471-4123-5_1(1-47)Online publication date: 31-May-2012
    • (2009)A survey of computer systems for expressive music performanceACM Computing Surveys (CSUR)10.1145/1592451.159245442:1(1-41)Online publication date: 14-Dec-2009

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