%0 Generic %1 kotecha2018generating %A Kotecha, Nikhil %A Young, Paul %D 2018 %K generation lstm music %T Generating Music using an LSTM Network %U http://arxiv.org/abs/1804.07300 %X 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.

@misc{kotecha2018generating, abstract = {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.}, added-at = {2018-04-23T12:41:29.000+0200}, author = {Kotecha, Nikhil and Young, Paul}, biburl = {https://www.bibsonomy.org/bibtex/2bbe492585a02b20324365d31620911ac/rcb}, description = {[1804.07300] Generating Music using an LSTM Network}, interhash = {5f480b74ae20ad817a09637c49ebe278}, intrahash = {bbe492585a02b20324365d31620911ac}, keywords = {generation lstm music}, note = {cite arxiv:1804.07300Comment: 8 pages, 11 figures}, timestamp = {2018-04-23T12:41:29.000+0200}, title = {Generating Music using an LSTM Network}, url = {http://arxiv.org/abs/1804.07300}, year = 2018 }