Designing and evaluating the usability of a machine learning API for rapid prototyping music technology

Bernardo, Francisco, Zbyszyński, Michael, Grierson, Mick and Fiebrink, Rebecca (2020) Designing and evaluating the usability of a machine learning API for rapid prototyping music technology. Frontiers in Artificial Intelligence, 3 (a13). pp. 1-18. ISSN 2624-8212

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Abstract

To better support creative software developers and music technologists' needs, and to empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. We review background research on the design and evaluation of application programming interfaces (APIs), with a focus on the domain of machine learning for music technology software development. We present the design rationale for the RAPID-MIX API, an easy-to-use API for rapid prototyping with interactive machine learning, and a usability evaluation study with software developers of music technology. A cognitive dimensions questionnaire was designed and delivered to a group of 12 participants who used the RAPID-MIX API in their software projects, including people who developed systems for personal use and professionals developing software products for music and creative technology companies. The results from the questionnaire indicate that participants found the RAPID-MIX API a machine learning API which is easy to learn and use, fun, and good for rapid prototyping with interactive machine learning. Based on these findings, we present an analysis and characterization of the RAPID-MIX API based on the cognitive dimensions framework, and discuss its design trade-offs and usability issues. We use these insights and our design experience to provide design recommendations for ML APIs for rapid prototyping of music technology. We conclude with a summary of the main insights, a discussion of the merits and challenges of the application of the CDs framework to the evaluation of machine learning APIs, and directions to future work which our research deems valuable.

Item Type: Article
Keywords: application programming interfaces, cognitive dimensions, music technology, interactive machine learning, user-centered design
Schools and Departments: School of Media, Film and Music > Music
Subjects: Q Science > Q Science (General) > Q0300 Cybernetics > Q0325 Self-organizing systems. Conscious automata > Q0325.5 Machine learning
Q Science > Q Science (General) > Q0300 Cybernetics > Q0325 Self-organizing systems. Conscious automata > Q0334 Artificial intelligence
T Technology > T Technology (General)
Depositing User: Jose Francisco Bernardo
Date Deposited: 27 Apr 2020 09:07
Last Modified: 27 Apr 2020 09:15
URI: http://sro.sussex.ac.uk/id/eprint/91049

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Project NameSussex Project NumberFunderFunder Ref
MIMIC: Musically Intelligent Machines Interacting CreativelyG2434AHRC-ARTS & HUMANITIES RESEARCH COUNCILAH/R002657/1
RAPID-MIXUnsetEU Horizon 2020644862