Designing and Evaluating the Usability of a Machine Learning API for Rapid Prototyping Music Technology.
Designing and Evaluating the Usability of a Machine Learning API for Rapid Prototyping Music Technology.
复制标题
设计和评估机器学习API用于快速原型音乐技术的可用性。
DOI：
10.3389/frai.2020.00013
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发表时间：
2020
影响因子：
4
通讯作者：
Fiebrink R
中科院分区：
文献类型：
--
作者：
Bernardo F;Zbyszyński M;Grierson M;Fiebrink R
为了更好地支持创意软件开发人员和音乐技术人员的需求，并使他们成为机器学习的用户和创新者，必须考虑并更好地理解机器学习工具的可用性和开发人员体验。我们回顾了应用程序编程接口（api）的设计和评估的背景研究，重点是音乐技术软件开发的机器学习领域。我们提出了rapid - mix API的设计原理，这是一个易于使用的API，用于交互式机器学习的快速原型设计，并与音乐技术的软件开发人员进行了可用性评估研究。我们设计了一份认知维度调查问卷，并将其交付给一组12名参与者，他们在软件项目中使用RAPID-MIX API，其中包括为个人使用开发系统的人员，以及为音乐和创意技术公司开发软件产品的专业人员。问卷调查的结果表明，参与者发现rapid - mix API是一个机器学习API，易于学习和使用，有趣，并且适合交互式机器学习的快速原型。基于这些发现，我们对基于认知维度框架的RAPID-MIX API进行了分析和表征，并讨论了其设计权衡和可用性问题。我们利用这些见解和我们的设计经验，为音乐技术的快速原型设计提供ML api的设计建议。最后，我们总结了主要见解，讨论了将cd框架应用于机器学习api评估的优点和挑战，以及我们的研究认为有价值的未来工作方向。
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 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.
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