Artificial Music Producer: Filtering Music Compositions by Artificial Taste
Human composers arrive at creative decisions on the basis of their individual musical taste. For automatic algorithmic composition, we propose to embrace that concept and encode taste as binary classification task. We identify and reconsider an implicit assumption: each and every result of a successful composing algorithm should be of great quality. In contrast, we formulate a general concept of composer-producer collaboration: an artificial music producer that filters 'good' and 'poor' results of an independent composer can improve musical quality without the need of refactoring composing strategies. That way, creative programming can be divided into independent subtasks, which allow for modular (multi-agent) system designs as well as productive team development. In a proof-of-concept experiment, we perform the discrimination of real Bach chorales from fakes generated by DeepBach using neural networks. This leads to an improvement of the overall results and provides possibilities to explain model behavior. Our concept can effortlessly be transferred to any pre-existing music generator.
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