Text-based LSTM Networks for Automatic Music Composition

descriptionPublicationkeyboard_double_arrow_right Conference object , Article , Preprint 01 Jan 2016Embargo end date: 01 Jan 2016Publisher:CSMCJournal:CoRR, volume abs/1604.05358Funded by:UKRI | Fusing Semantic and Audio...

Authors: Keunwoo Choi; György Fazekas; Mark B. Sandler;

doi: 10.5281/zenodo.4289324 , 10.48550/arxiv.1604.05358 , 10.5281/zenodo.4289325

arXiv: 1604.05358

Text-based LSTM Networks for Automatic Music Composition

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Abstract

In this paper, we introduce new methods and discuss results of text-based LSTM (Long Short-Term Memory) networks for automatic music composition. The proposed network is designed to learn relationships within text documents that represent chord progressions and drum tracks in two case studies. In the experiments, word-RNNs (Recurrent Neural Networks) show good results for both cases, while character-based RNNs (char-RNNs) only succeed to learn chord progressions. The proposed system can be used for fully automatic composition or as semi-automatic systems that help humans to compose music by controlling a diversity parameter of the model.

Accepted in the 1st Conference on Computer Simulation of Musical Creativity, 2016

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Keywords

FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Science - Multimedia, Multimedia (cs.MM)

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