Generating Music using an LSTM Network
This addresses the challenge of automated music composition for applications in creative AI, but it is incremental as it builds on existing LSTM methods.
The paper tackled the problem of generating polyphonic music by developing a Bi-axial LSTM neural network that predicts and composes music aligned with musical rules, achieving good performance in both quantitative and qualitative analyses.
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.