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Machine learning research that matters for music creation: A case study
2018
Journal of New Music Research
Corresponding author 1 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
doi:10.1080/09298215.2018.1515233
fatcat:fxhtrzpfazd2dnv7y435dwfi4q