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    Text-based LSTM networks for Automatic Music Composition 
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    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.
    Authors
    CHOI, K; sandler, M; fazekas, G; Conference on Computer Simulation of Musical Creativity
    URI
    http://qmro.qmul.ac.uk/xmlui/handle/123456789/12552
    Collections
    • Electronic Engineering and Computer Science [2944]
    Licence information
    arXiv record http://arxiv.org/abs/1604.05358. Presented at Conference on Computer Simulation of Musical Creativity
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