Finding temporal structure in music: Blues improvisation with LSTM recurrent networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

123 Scopus citations

Abstract

We consider the problem of extracting essential ingredients of music signals, such as a well-defined global temporal structure in the form of nested periodicities (or meter). We investigate whether we can construct an adaptive signal processing device that learns by example how to generate new instances of a given musical style. Because recurrent neural networks (RNNs) can, in principle, learn the temporal structure of a signal, they are good candidates for such a task. Unfortunately, music composed by standard 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 and counting and the learning of context sensitive languages. 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.
Original languageEnglish (US)
Title of host publicationNeural Networks for Signal Processing - Proceedings of the IEEE Workshop
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages747-756
Number of pages10
ISBN (Print)0780376161
DOIs
StatePublished - Jan 1 2002
Externally publishedYes

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