Toward meaningful algorithmic music-making for non-programmers

Matthew Francis Bellingham, Simon Holland, Paul Mulholland

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Algorithmic composition typically involves manipulating structural elements such as indeterminism, parallelism, choice, multi-choice, recursion, weighting, sequencing, timing, and looping. There exist powerful tools for these purposes, however, many musicians who are not expert programmers find such tools inaccessible and difficult to understand and use. By analysing a representative selection of user interfaces for algorithmic composition, through the use of the Cognitive Dimensions of Notations (CDN) and other analytical tools, we identified candidate design principles, and applied these principles to create and implement a new visual formalism, programming abstraction and execution model. The resulting visual programming language, Choosers, is designed to allow ready visualisation and manipulation of structural elements of the kind involved in algorithmic music composition, while making minimal demand on programming ability. Programming walkthroughs with novice users were used iteratively to refine and validate diverse aspects of the design. Currently, workshops with musical experts and teachers are being conducted to explore the value of the language for varied pragmatic purposes by expressing, manipulating and reflecting on diverse musical examples.
Original languageEnglish
Title of host publicationProceedings of the 30th Annual Workshop of the Psychology of Programming Interest Group (PPIG 2019)
EditorsMariana Marasoiu, Luke Church, Lindsay Marshall
PublisherPsychology of Programming Interest Group
Number of pages10
Publication statusPublished - 30 Aug 2019


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