Bard: Software Development for Algorithmic Composition: Making Music out of Chess Games in Real-Time
Translated title
Bard: Software Development for Algorithmic Composition
Author
Papageorgiou, Emmanouil
Term
4. Term
Education
Publication year
2022
Pages
49
Abstract
Dette projekt undersøger, hvordan et skakparti kan omsættes til musik, der udfolder sig samtidig med spillet. Det kombinerer to typer information: (1) skaknotationen, dvs. registreringen af træk, og (2) beregninger fra en skakmotor, et program der analyserer stillinger. På denne baggrund udvikles en metode til algoritmisk komposition—en regelbaseret måde at generere musik på—som skal give en sammenhængende musikalsk fortælling til et parti. Ved at behandle skakdata som funktioner af musikkens tonale system (forhold mellem toner og harmonik) er målet, at den sonificerede lyd kan fremkalde følelser, der svarer til, hvordan spillere oplever stillingerne. For at afprøve metoden blev to softwareversioner sammenlignet i en blind test: én, der virkelig reagerede på deltagerens træk på brættet, og én, der i stedet fulgte en forudindspillet række træk og derfor ikke reagerede på deltageren. Deltagerne brugte begge versioner og besvarede de samme spørgsmål om deres oplevelse uden at vide, hvilken version var interaktiv. Den interaktive algoritme fik højere vurderinger end den foruddefinerede i alle spørgsmål, hvilket tyder på, at det musikalske resultat kan opfattes som meningsfuldt forbundet med spillet.
This project explores how a chess game can be turned into music that unfolds alongside the play. It combines two kinds of information: (1) chess notation, the record of moves, and (2) calculations from a chess engine, a program that analyzes positions. Using these inputs, the project develops a method for algorithmic composition—a rule-based way to generate music—aimed at creating a coherent musical narration of a game. By processing chess data through the musical tonal system (relationships of pitch and harmony), the resulting sonification is intended to evoke feelings that relate to how players experience positions. To test the method, two software versions were compared in a blind setup: one that truly responded to the participant’s moves on the board, and another that followed a pre-recorded sequence of moves and did not respond to the participant. Participants used both versions and answered the same questions about their experience without knowing which one was interactive. The interactive algorithm received higher ratings than the predefined version on all questions, suggesting that the musical output can be perceived as meaningfully connected to the game.
[This summary has been rewritten with the help of AI based on the project's original abstract]
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