Machine learning research that matters for music creation: A case study. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Sturm, Bob L.; Ben-Tal, Oded; Monaghan, Úna; Collins, Nick; Herremans, Dorien; Chew, Elaine; Hadjeres, Gaëtan; Deruty, Emmanuel; Pachet, François
• Is Part Of: Journal of new music research. Volume 48:Issue 1(2019)
• Page Start: 36
• Page End: 55
• Publication Date: 2019-01-01
• Subjects: Applied machine learning -- music generation -- computational creativity -- folk music; Music -- Periodicals; Musicology -- Periodicals; 780.72
• DOI: 10.1080/09298215.2018.1515233
• Languages: English
• ISSNs: 0929-8215

Abstract

ABSTRACT: Research applying machine learning to music modelling 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.