%0 Conference Paper %1 Eck02findingtemporal %A Eck, Douglas %A Schmidhuber, Jürgen %B NEURAL NETWORKS FOR SIGNAL PROCESSING XII, PROCEEDINGS OF THE 2002 IEEE WORKSHOP %D 2002 %I IEEE %K application machine-learning music neural-networks recurrent-neural-networks %P 747--756 %T Finding temporal structure in music: Blues improvisation with LSTM recurrent networks %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.2656 %X Few types of signal streams are as ubiquitous as music. Here we consider the problem of extracting essential ingredients of music signals, such as well-defined global temporal structure in the form of nested periodicities (or meter). Can we construct an adaptive signal processing device that learns by example how to generate new instances of a given musical style? Because recurrent neural networks can in principle learn the temporal structure of a signal, they are good candidates for such a task. Unfortunately, music composed by standard recurrent neural networks (RNNs) often lacks global coherence. 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 learning of context sensitive languages. In the current study we show that LSTM is also a good mechanism for learning to compose music. We present experimental results showing that LSTM successfully learns a form of blues music and is able to compose novel (and we 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.

@inproceedings{Eck02findingtemporal, abstract = { Few types of signal streams are as ubiquitous as music. Here we consider the problem of extracting essential ingredients of music signals, such as well-defined global temporal structure in the form of nested periodicities (or meter). Can we construct an adaptive signal processing device that learns by example how to generate new instances of a given musical style? Because recurrent neural networks can in principle learn the temporal structure of a signal, they are good candidates for such a task. Unfortunately, music composed by standard recurrent neural networks (RNNs) often lacks global coherence. 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 learning of context sensitive languages. In the current study we show that LSTM is also a good mechanism for learning to compose music. We present experimental results showing that LSTM successfully learns a form of blues music and is able to compose novel (and we 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.}, added-at = {2011-08-12T10:19:48.000+0200}, author = {Eck, Douglas and Schmidhuber, Jürgen}, biburl = {https://www.bibsonomy.org/bibtex/2a67328ff553022857113ce7456a3d7cf/gromgull}, booktitle = {NEURAL NETWORKS FOR SIGNAL PROCESSING XII, PROCEEDINGS OF THE 2002 IEEE WORKSHOP}, description = {CiteSeerX — Finding temporal structure in music: Blues improvisation with LSTM recurrent networks}, interhash = {36baf273a5e22466c2273d28e5c17e33}, intrahash = {a67328ff553022857113ce7456a3d7cf}, keywords = {application machine-learning music neural-networks recurrent-neural-networks}, pages = {747--756}, publisher = {IEEE}, timestamp = {2011-08-12T10:19:48.000+0200}, title = {Finding temporal structure in music: Blues improvisation with LSTM recurrent networks}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.2656}, year = 2002 }