Issues and techniques for collaborative music making on multi-touch surfaces

A range of systems exist for collaborative music making on multi-touch surfaces. Some of them have been highly successful, but currently there is no systematic way of designing them, to maximise collaboration for a particular user group. We are particularly interested in systems that will engage novices and experts. We designed a simple application in an initial attempt to clearly analyse some of the issues. Our application allows groups of users to express themselves in collaborative music making using pre-composed materials. User studies were video recorded and analysed using two techniques derived from Grounded Theory and Content Analysis. A questionnaire was also conducted and evaluated. Findings suggest that the application affords engaging interaction. Enhancements for collaborative music making on multi-touch surfaces are discussed. Finally, future work on the prototype is proposed to maximise engagement

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

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

Graduate Catalog, 1970-1971

https://scholar.valpo.edu/gradcatalogs/1006/thumbnail.jp

Graduate Catalog, 1967-1968

https://scholar.valpo.edu/gradcatalogs/1004/thumbnail.jp

Evaluation of live human-computer music-making: Quantitative and qualitative approaches

NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Human-Computer Studies. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Human-Computer Studies, [VOL 67,ISS 11(2009)] DOI: 10.1016/j.ijhcs.2009.05.00