%0 Report %1 Eck2002 %A Eck, D. %A Schmidhuber, J. %C www.idsia.ch/\-techrep.html %D 2002 %K imported %N IDSIA-07-02 %T A First Look at Music Composition using LSTM Recurrent Neural Networks %X In general music composed by recurrent neural networks (RNNs) suffers from a lack of global structure. Though networks can learn note-by-note transition probabilities and even reproduce phrases, attempts at learning an entire musical form and using that knowledge to guide composition have been unsuccessful. The reason for this failure seems to be that RNNs cannot keep track of temporally distant events that indicate global music structure. Long Short-Term Memory (LSTM) has succeeded in similar domains where other RNNs have failed, such as timing & counting and CSL learning. In the current study I show that LSTM is also a good mechanism for learning to compose music. I compare this approach to previous attempts, with particular focus on issues of data representation. I present experimental results showing that LSTM successfully learns a form of blues music and is able to compose novel (and I believe pleasing) melodies in that style. Remarkably, once the network has found the relevant structure it does not drift from it: LSTM is able to play the blues with good timing and proper structure as long as one is willing to listen. Note: This is a more complete version of the 2002 ICANN submission Learning the Long-Term Structure of the Blues.

@techreport{Eck2002, abstract = {In general music composed by recurrent neural networks (RNNs) suffers from a lack of global structure. Though networks can learn note-by-note transition probabilities and even reproduce phrases, attempts at learning an entire musical form and using that knowledge to guide composition have been unsuccessful. The reason for this failure seems to be that RNNs cannot keep track of temporally distant events that indicate global music structure. Long Short-Term Memory (LSTM) has succeeded in similar domains where other RNNs have failed, such as timing \& counting and CSL learning. In the current study I show that LSTM is also a good mechanism for learning to compose music. I compare this approach to previous attempts, with particular focus on issues of data representation. I present experimental results showing that LSTM successfully learns a form of blues music and is able to compose novel (and I believe pleasing) melodies in that style. Remarkably, once the network has found the relevant structure it does not drift from it: LSTM is able to play the blues with good timing and proper structure as long as one is willing to listen. {\em Note: This is a more complete version of the 2002 ICANN submission Learning the Long-Term Structure of the Blues.}}, added-at = {2010-02-27T01:05:18.000+0100}, address = {www.idsia.ch/\-techrep.html}, author = {Eck, D. and Schmidhuber, J.}, biburl = {https://www.bibsonomy.org/bibtex/2e572b0e86b16d70f9fb3f4c12f0c178a/tb2332}, file = {IDSIA-07-02.ps.gz:ftp\://ftp.idsia.ch/pub/techrep/IDSIA-07-02.ps.gz:PostScript}, institution = {IDSIA}, interhash = {aa2497c5d75e266c76f342c79a160987}, intrahash = {e572b0e86b16d70f9fb3f4c12f0c178a}, keywords = {imported}, month = {March}, number = {IDSIA-07-02}, owner = {thierry}, source = {OwnPublication}, timestamp = {2010-02-27T01:05:21.000+0100}, title = {A First Look at Music Composition using {LSTM} Recurrent Neural Networks}, year = 2002 }