Machine learning research that matters for music creation: A case study

Author

Sturm, Bob L and Ben-Tal, Oded and Monaghan, Úna and Collins, Nick and Herremans, Dorien and Chew, Elaine and Hadjeres, Gaëtan and Deruty, Emmanuel and Pachet, François

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.