research-article

Evolving in-game mood-expressive music with MetaCompose

Authors:

Julian Togelius,

Sebastian RisiAuthors Info & Claims

AM '18: Proceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion

Article No.: 8, Pages 1 - 8

https://doi.org/10.1145/3243274.3243292

Published: 12 September 2018 Publication History

Abstract

MetaCompose is a music generator based on a hybrid evolutionary technique that combines FI-2POP and multi-objective optimization. In this paper we employ the MetaCompose music generator to create music in real-time that expresses different mood-states in a game-playing environment (Checkers). In particular, this paper focuses on determining if differences in player experience can be observed when: (i) using affective-dynamic music compared to static music, and (ii) the music supports the game's internal narrative/state. Participants were tasked to play two games of Checkers while listening to two (out of three) different set-ups of game-related generated music. The possible set-ups were: static expression, consistent affective expression, and random affective expression. During game-play players wore a E4 Wristband, allowing various physiological measures to be recorded such as blood volume pulse (BVP) and electromyographic activity (EDA). The data collected confirms a hypothesis based on three out of four criteria (engagement, music quality, coherency with game excitement, and coherency with performance) that players prefer dynamic affective music when asked to reflect on the current game-state. In the future this system could allow designers/composers to easily create affective and dynamic soundtracks for interactive applications.

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Cited By

Usman FAnwar SRauf U(2023)Visual Recognition for ZELDA Content Generation via Generative Adversarial Network2023 3rd International Conference on Artificial Intelligence (ICAI)10.1109/ICAI58407.2023.10136680(76-81)Online publication date: 22-Feb-2023
https://doi.org/10.1109/ICAI58407.2023.10136680
Kopel MAntczak DWalczyński M(2023)Generating Music for Video Games with Real-Time Adaptation to Gameplay PaceIntelligent Information and Database Systems10.1007/978-981-99-5834-4_21(261-272)Online publication date: 24-Jul-2023
https://dl.acm.org/doi/10.1007/978-981-99-5834-4_21
Plut CPasquier PEns JTchemeube R(2022)PreGLAM-MMMProceedings of the 17th International Conference on the Foundations of Digital Games10.1145/3555858.3555947(1-11)Online publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1145/3555858.3555947
Show More Cited By

Index Terms

Evolving in-game mood-expressive music with MetaCompose
1. Applied computing
  1. Arts and humanities
    1. Sound and music computing
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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cover image ACM Other conferences

AM '18: Proceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion

September 2018

252 pages

ISBN:9781450366090

DOI:10.1145/3243274

Conference Chair:
Stuart Cunningham,
Program Chair:
Richard Picking

Copyright © 2018 ACM.

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Publication History

Published: 12 September 2018

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AM'18

AM'18: Sound in Immersion and Emotion

September 12 - 14, 2018

Wrexham, United Kingdom

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Overall Acceptance Rate 177 of 275 submissions, 64%

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Cited By

Usman FAnwar SRauf U(2023)Visual Recognition for ZELDA Content Generation via Generative Adversarial Network2023 3rd International Conference on Artificial Intelligence (ICAI)10.1109/ICAI58407.2023.10136680(76-81)Online publication date: 22-Feb-2023
https://doi.org/10.1109/ICAI58407.2023.10136680
Kopel MAntczak DWalczyński M(2023)Generating Music for Video Games with Real-Time Adaptation to Gameplay PaceIntelligent Information and Database Systems10.1007/978-981-99-5834-4_21(261-272)Online publication date: 24-Jul-2023
https://dl.acm.org/doi/10.1007/978-981-99-5834-4_21
Plut CPasquier PEns JTchemeube R(2022)PreGLAM-MMMProceedings of the 17th International Conference on the Foundations of Digital Games10.1145/3555858.3555947(1-11)Online publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1145/3555858.3555947
Makhmutov MBrown JSurkov MTimchenko ATimchenko K(2022)Adaptive Game Soundtrack Tempo Based on Players’ Actions2022 IEEE Conference on Games (CoG)10.1109/CoG51982.2022.9893604(441-448)Online publication date: 21-Aug-2022
https://dl.acm.org/doi/10.1109/CoG51982.2022.9893604
Chen NWen G(2021)WITHDRAWN: Music Composition Feasibility using a Quality Classification Model based on Artificial IntelligenceAggression and Violent Behavior10.1016/j.avb.2021.101632(101632)Online publication date: Jun-2021
https://doi.org/10.1016/j.avb.2021.101632
Liu JSnodgrass SKhalifa ARisi SYannakakis GTogelius J(2021)Deep learning for procedural content generationNeural Computing and Applications10.1007/s00521-020-05383-833:1(19-37)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s00521-020-05383-8
Carnovalini FRodà A(2020)Computational Creativity and Music Generation Systems: An Introduction to the State of the ArtFrontiers in Artificial Intelligence10.3389/frai.2020.000143Online publication date: 3-Apr-2020
https://doi.org/10.3389/frai.2020.00014
Washburn MKhosmood F(2020)Dynamic Procedural Music Generation from NPC AttributesProceedings of the 15th International Conference on the Foundations of Digital Games10.1145/3402942.3409785(1-4)Online publication date: 15-Sep-2020
https://dl.acm.org/doi/10.1145/3402942.3409785
Loughran RO’Neill M(2020)Evolutionary music: applying evolutionary computation to the art of creating musicGenetic Programming and Evolvable Machines10.1007/s10710-020-09380-721:1-2(55-85)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1007/s10710-020-09380-7
Rodrigues LBrancher J(2019)Procedurally generating a digital math game’s levels: Does it impact players’ in-game behavior?Entertainment Computing10.1016/j.entcom.2019.10032532(100325)Online publication date: Dec-2019
https://doi.org/10.1016/j.entcom.2019.100325

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