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arXiv · 2006.13329

Bach or Mock? A Grading Function for Chorales in the Style of J.S. Bach

Alexander Fang·Alisa Liu·Prem Seetharaman·Bryan Pardo
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Abstract

Deep generative systems that learn probabilistic models from a corpus of existing music do not explicitly encode knowledge of a musical style, compared to traditional rule-based systems. Thus, it can be difficult to determine whether deep models generate stylistically correct output without expert evaluation, but this is expensive and time-consuming. Therefore, there is a need for automatic, interpretable, and musically-motivated evaluation measures of generated music. In this paper, we introduce a grading function that evaluates four-part chorales in the style of J.S. Bach along important musical features. We use the grading function to evaluate the output of a Transformer model, and show that the function is both interpretable and outperforms human experts at discriminating Bach chorales from model-generated ones.

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Alexander Fang, Alisa Liu, Prem Seetharaman, Bryan Pardo. 2020-06-23. Bach or Mock? A Grading Function for Chorales in the Style of J.S. Bach. https://arxiv.org/abs/2006.13329

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