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Designing and Evaluating the Usability of a Machine Learning API for Rapid Prototyping Music Technology.

Francisco Bernardo1,2, Michael Zbyszyński2, Mick Grierson2,3

  • 1EMuTe Lab, School of Media, Film and Music, University of Sussex, Brighton, United Kingdom.

Frontiers in Artificial Intelligence
|March 18, 2021
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Summary

The RAPID-MIX API enhances machine learning (ML) for music tech developers, offering an easy-to-use tool for rapid prototyping. User studies confirm its usability and suitability for interactive ML applications.

Keywords:
application programming interfacescognitive dimensionsinteractive machine learningmusic technologyuser-centered design

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Area of Science:

  • Human-Computer Interaction
  • Machine Learning Engineering
  • Music Technology Software Development

Background:

  • Usability and developer experience are crucial for machine learning (ML) tools in creative software.
  • Existing research on application programming interface (API) design and evaluation is relevant to ML for music technology.
  • Understanding developer needs empowers them as ML users and innovators.

Purpose of the Study:

  • To evaluate the usability and developer experience of ML tools for music technology.
  • To present the design rationale and evaluation of the RAPID-MIX API for rapid prototyping.
  • To provide design recommendations for ML APIs in music technology.

Main Methods:

  • Literature review of API design and evaluation in ML for music technology.
  • Design and implementation of the RAPID-MIX API for interactive ML.
  • Usability evaluation using a cognitive dimensions questionnaire with 12 music technology software developers.

Main Results:

  • Participants found the RAPID-MIX API easy to learn and use.
  • The API was perceived as fun and effective for rapid prototyping with interactive ML.
  • Analysis using the cognitive dimensions framework identified design trade-offs and usability issues.

Conclusions:

  • The RAPID-MIX API is a usable and effective tool for rapid prototyping in music technology.
  • The cognitive dimensions framework can be applied to evaluate ML APIs, offering valuable insights.
  • Design recommendations are provided for future ML APIs aimed at music technology rapid prototyping.