A corpus-based hybrid approach to music analysis and composition

We present a corpus-based hybrid approach to music analysis and composition, which incorporates statistical, connectionist, and evolutionary components. Our framework employs artificial music critics, which may be trained on large music corpora, and then pass aesthetic judgment on music artifacts. Music artifacts are generated by an evolutionary music composer, which utilizes music critics as fitness functions. To evaluate this approach we conducted three experiments. First, using music features based on Zipf's law, we trained artificial neural networks to predict the popularity of 992 musical pieces with 87.85% accuracy, Then, assuming that popularity correlates with aesthetics, we incorporated such neural networks into a genetic-programming system, called NEvMuse. NEvMuse autonomously "composed" novel variations of J.S. Bach's Invention #13 in A minor (BWV 784), variations which many listeners found to be aesthetically pleasing. Finally, we compared aesthetic judgments from an artificial music critic with emotional responses from 23 human subjects. Significant correlations were found. We provide evaluation results and samples of generated music. These results have implications for music information retrieval and computer-aided music composition.