Machine learning research that matters for music creation: A case study
Machine learning research that matters for music creation: A case study
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DOI：
10.1080/09298215.2018.1515233
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发表时间：
2019-01-01
影响因子：
1.1
通讯作者：
Pachet, Francois
中科院分区：
文献类型：
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作者：
Sturm, Bob L.;Ben-Tal, Oded;Pachet, Francois
将机器学习应用于音乐建模和生成的研究通常提出模型体系结构、训练方法和数据集，并使用诸如序列可能性和/或定性听力测试之类的定量测量来评估系统性能。这类工作很少明确地质疑和分析其对现实世界从业人员的有用性和影响，然后以这些结果为基础，为机器学习的发展和应用提供信息。本文试图为机器学习在音乐创作中的应用做一些尝试。我们与实践者一起开发和使用了几个用于音乐创作的机器学习应用程序，并展示了结果的公开音乐会。我们对整个经验进行反思，得出几种方法来推动机器学习在音乐创作中的这些和类似的应用。
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.