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Generating Music using an LSTM Network
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A model of music needs to have the ability to recall past details and have a clear, coherent understanding of musical structure. Detailed in the paper is a neural network architecture that predicts and generates polyphonic music aligned with musical rules. The probabilistic model presented is a Bi-axial LSTM trained with a kernel reminiscent of a convolutional kernel. When analyzed quantitatively and qualitatively, this approach performs well in composing polyphonic music. Link to the code is provided.
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Cited by 1 Pith paper
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LSTM Based Music Generation System
A single-layer LSTM learns to predict note sequences from a small pop MIDI dataset, reaching a training log-likelihood of -6.23 in 54 minutes.
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