Taking the Models back to Music Practice: Evaluating Generative Transcription Models built using Deep Learning-猫眼课题宝

DOI：

10.5920/jcms.2017.09

发表时间：

2017-09

期刊：

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其他

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作者：

Bob L. Sturm;Oded Ben-Tal

我们扩展了我们对Sturm，桑托斯，Ben-Tal和Korshunova（2016）首次提出的音乐transmittance生成模型的评估。我们从五个方面对模型进行了评估：1）在群体层面上，将30，000个生成的音符与23，000多个训练音符的统计数据进行比较; 2）在实践层面上，考察特定生成音符作为音乐作品的成功方式; 3）作为一个“邪恶的测试者”，寻求模型的音乐知识极限; 4）在辅助音乐创作的背景下，使用模型在训练数据的约定内创作音乐;最后，5）将模型带到现实世界的音乐从业者中。我们的工作试图展示评估机器学习方法在建模和制作音乐中的应用的新方法，以及将结果带回音乐实践领域以判断其有用性的重要性。我们的数据集和软件是开放的，可在https://github.com/IraKorshunova/folk-rnn获得。

We extend our evaluation of generative models of music transcriptions that were first presented in Sturm, Santos, Ben-Tal, and Korshunova (2016). We evaluate the models in five different ways: 1) at the population level, comparing statistics of 30,000 generated transcriptions with those of over 23,000 training transcriptions; 2) at the practice level, examining the ways in which specific generated transcriptions are successful as music compositions; 3) as a “nefarious tester”, seeking the music knowledge limits of the models; 4) in the context of assisted music composition, using the models to create music within the conventions of the training data; and finally, 5) taking the models to real-world music practitioners. Our work attempts to demonstrate new approaches to evaluating the application of machine learning methods to modelling and making music, and the importance of taking the results back to the realm of music practice to judge their usefulness. Our datasets and software are open and available at https://github.com/IraKorshunova/folk-rnn.

Taking the Models back to Music Practice: Evaluating Generative Transcription Models built using Deep Learning

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