Evolving in-game mood-expressive music with MetaCompose

Marco Scirea, Julian Togelius, Peter Eklund, Sebastian Risi

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


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.

Original languageEnglish
Title of host publicationProceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion
Number of pages8
PublisherAssociation for Computing Machinery
Publication date12. Sep 2018
Article number8
ISBN (Electronic)978-1-4503-6609-0
Publication statusPublished - 12. Sep 2018
EventAudio Mostly 2018 on Sound in Immersion and Emotion - Wrexham, United Kingdom
Duration: 12. Sep 201814. Sep 2018


ConferenceAudio Mostly 2018 on Sound in Immersion and Emotion
Country/TerritoryUnited Kingdom


  • Affective expression
  • Evolutionary algorithms
  • Music generation


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