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Anticipation-RNN

swMATH ID: 45502
Software Authors: Ga√ętan Hadjeres; Frank Nielsen
Description: Anticipation-RNN: enforcing unary constraints in sequence generation, with application to interactive music generation. Recurrent neural networks (RNNs) are now widely used on sequence generation tasks due to their ability to learn long-range dependencies and to generate sequences of arbitrary length. However, their left-to-right generation procedure only allows a limited control from a potential user which makes them unsuitable for interactive and creative usages such as interactive music generation. This article introduces a novel architecture called anticipation-RNN which possesses the assets of the RNN-based generative models while allowing to enforce user-defined unary constraints. We demonstrate its efficiency on the task of generating melodies satisfying unary constraints in the style of the soprano parts of the J.S. Bach chorale harmonizations. Sampling using the anticipation-RNN is of the same order of complexity than sampling from the traditional RNN model. This fast and interactive generation of musical sequences opens ways to devise real-time systems that could be used for creative purposes.
Homepage: https://link.springer.com/article/10.1007/s00521-018-3868-4
Source Code:  https://github.com/Ghadjeres/Anticipation-RNN
Dependencies: Python
Keywords: Recurrent neural networks; RNNs; music generation
Related Software: jSymbolic; SampleRNN; MidiNet; LakhNES; Music Transformer; MorpheuS; MuseGAN; BERT; Adam
Cited in: 1 Document

Cited in 1 Serial

1 Machine Learning

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