Algorithmic composition of polyphonic music with the WaveCRF
[S.l. : s.n.]
In2017 Neural Information Processing Systems (NIPS): Machine Learning for Creativity and Design Workshop (December 8, 2017), pp. 1-3
NIPS 2017: 31st Annual Conference on Neural Information Processing Systems (Long Beach, California, December 4-9, 2017)
Article in monograph or in proceedings
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2017 Neural Information Processing Systems (NIPS): Machine Learning for Creativity and Design Workshop (December 8, 2017)
SubjectAction, intention, and motor control; Cognitive artificial intelligence; DI-BCB_DCC_Theme 2: Perception, Action and Control; DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication
Here, we propose a new approach for modeling conditional probability distributions of polyphonic music by combining WaveNET and CRF-RNN variants, and show that this approach beats LSTM and WaveNET baselines that do not take into account the statistical dependencies between simultaneous notes.
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