Criticism, culture, and the automatic generation of artworks
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Frankensteinian methods for evolutionary music composition
Musical networks
Zipf's Law, Music Classification, and Aesthetics
Computer Music Journal
Evolutionary Computer Music
On the development of critics in evolutionary computation artists
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Technical Section: On the development of evolutionary artificial artists
Computers and Graphics
The Evolution of Evolutionary Software: Intelligent Rhythm Generation in Kinetic Engine
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Evolutionary music composition based on Zipf's law
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Increasing efficiency and quality in the automatic composition of three-move mate problems
ICEC'11 Proceedings of the 10th international conference on Entertainment Computing
EvoMUSART'12 Proceedings of the First international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
Genetic evolution of L and FL-systems for the production of rhythmic sequences
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Learning aesthetic judgements in evolutionary art systems
Genetic Programming and Evolvable Machines
AI methods in algorithmic composition: a comprehensive survey
Journal of Artificial Intelligence Research
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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.