Abstract
We describe a neural network architecture which enables prediction and composition of polyphonic music in a manner that preserves translation-invariance of the dataset. Specifically, we demonstrate training a probabilistic model of polyphonic music using a set of parallel, tied-weight recurrent networks, inspired by the structure of convolutional neural networks. This model is designed to be invariant to transpositions, but otherwise is intentionally given minimal information about the musical domain, and tasked with discovering patterns present in the source dataset. We present two versions of the model, denoted TP-LSTM-NADE and BALSTM, and also give methods for training the network and for generating novel music. This approach attains high performance at a musical prediction task and successfully creates note sequences which possess measure-level musical structure.
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Acknowledgments
We would like to thank Dr. Robert Keller for helpful discussions and advice. We would also like to thank the developers of the Theano framework [20], which we used to run our experiments, as well as Harvey Mudd College for providing computing resources. This work used the Extreme Science and Engineering Discovery Environment (XSEDE) [23], which is supported by National Science Foundation grant number ACI-1053575.
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Johnson, D.D. (2017). Generating Polyphonic Music Using Tied Parallel Networks. In: Correia, J., Ciesielski, V., Liapis, A. (eds) Computational Intelligence in Music, Sound, Art and Design. EvoMUSART 2017. Lecture Notes in Computer Science(), vol 10198. Springer, Cham. https://doi.org/10.1007/978-3-319-55750-2_9
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DOI: https://doi.org/10.1007/978-3-319-55750-2_9
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