| 1. |
- Agres, Kat Rose, et al.
(author)
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Music, Computing, and Health: A roadmap for the current and future roles of music technology for health care and well-being
- 2021
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Other publication (other academic/artistic)abstract
- The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop ‘Music, Computing, and Health’ was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech) and robotics. Following the discussions at the workshop, this paper provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the paper summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that 1) supports person-centered care and evidence-based treatments, and 2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The paper provides a resource for those seeking toengage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field.
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| 2. |
- Sturm, Bob, 1975-, et al.
(author)
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Machine Learning Research that Matters for Music Creation : A Case Study
- 2019
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In: Journal of New Music Research. - : Taylor & Francis. - 0929-8215 .- 1744-5027. ; 48:1, s. 36-55
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Journal article (peer-reviewed)abstract
- Research applying machine learning to music modeling and generation typically proposes model architectures, training methods and datasets, and gauges system performance using quantitative measures like sequence likelihoods and/or qualitative listening tests. Rarely does such work explicitly question and analyse its usefulness for and impact on real-world practitioners, and then build on those outcomes to inform the development and application of machine learning. This article attempts to do these things for machine learning applied to music creation. Together with practitioners, we develop and use several applications of machine learning for music creation, and present a public concert of the results. We reflect on the entire experience to arrive at several ways of advancing these and similar applications of machine learning to music creation.
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