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

The Art of Engaging: Implications for Computer Music Systems

The art of engaging with computer music systems is multifaceted. This paper will provide an overview of the issues of interface between musician and computer, cognitive aspects of engagement as involvement, and metaphysical understandings of engagement as proximity. Finally, this paper will examine implications for the design of computer music systems when these issues are taken into account

Multi-touch interaction principles for collaborative real-time music activities: towards a pattern language

In this paper we give an analysis of the literature on a set of problems that can arise when undertaking the interaction design of multi-touch applications for collaborative real-time music activities, which are designed for multitouch technologies (e.g. smartphones, tablets, interactive tabletops, among others). Each problem is described, and a candidate design pattern (CDP) is suggested in the form of a short sentence and a diagram—an approach inspired by Christopher Alexander’s A Pattern Language. These solutions relate to the fundamental collaborative principles of democratic relationships, identities and collective interplay. We believe that this approach might disseminate forms of best design practice for collaborative music applications, in order to produce real-time musical systems which are collaborative and expressive

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

Implementing feedback in creative systems : a workshop approach

One particular challenge in AI is the computational modelling and simulation of creativity. Feedback and learning from experience are key aspects of the creative process. Here we investigate how we could implement feedback in creative systems using a social model. From the field of creative writing we borrow the concept of a Writers Workshop as a model for learning through feedback. The Writers Workshop encourages examination, discussion and debates of a piece of creative work using a prescribed format of activities. We propose a computational model of the Writers Workshop as a roadmap for incorporation of feedback in artificial creativity systems. We argue that the Writers Workshop setting describes the anatomy of the creative process. We support our claim with a case study that describes how to implement the Writers Workshop model in a computational creativity system. We present this work using patterns other people can follow to implement similar designs in their own systems. We conclude by discussing the broader relevance of this model to other aspects of AI