Аннотация
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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