Corpus ID: 3226639

Computer Assisted Composition with Recurrent Neural Networks

  title={Computer Assisted Composition with Recurrent Neural Networks},
  author={Christian J. Walder and Dongwoo Kim},
Sequence modeling with neural networks has lead to powerful models of symbolic music data. We address the problem of exploiting these models to reach creative musical goals, by combining with human input. To this end we generalise previous work, which sampled Markovian sequence models under the constraint that the sequence belong to the language of a given finite state machine provided by the human. We consider more expressive non-Markov models, thereby requiring approximate sampling which we… Expand
Computer Assisted Composition in Continuous Time
This work introduces a simple, novel, and efficient particle filter scheme, applicable to general continuous time point processes, and demonstrates that in comparison with a more traditional beam search baseline, the particle filter exhibits superior statistical properties and yields more agreeable results in an extensive human listening test experiment. Expand
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A representation which reduces polyphonic music to a univariate categorical sequence is introduced, which is able to apply state of the art natural language processing techniques, namely the long short-term memory sequence model. Expand
Neural Network Music Composition by Prediction: Exploring the Benefits of Psychoacoustic Constraints and Multi-scale Processing
  • M. Mozer
  • Computer Science, Engineering
  • Connect. Sci.
  • 1994
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This work investigates how artificial neural networks can be trained on a large corpus of melodies and turned into automated composers able to generate new melodies coherent with the style they have been trained on. Expand
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DeepBach, a graphical model aimed at modeling polyphonic music and specifically hymn-like pieces, is introduced, which is capable of generating highly convincing chorales in the style of Bach. Expand
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A probabilistic model based on distribution estimators conditioned on a recurrent neural network that is able to discover temporal dependencies in high-dimensional sequences that outperforms many traditional models of polyphonic music on a variety of realistic datasets is introduced. Expand
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