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2017 | OriginalPaper | Chapter

Combining LSTM and Feed Forward Neural Networks for Conditional Rhythm Composition

Authors : Dimos Makris, Maximos Kaliakatsos-Papakostas, Ioannis Karydis, Katia Lida Kermanidis

Published in: Engineering Applications of Neural Networks

Publisher: Springer International Publishing

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Abstract

Algorithmic music composition has long been in the spotlight of music information research and Long Short-Term Memory (LSTM) neural networks have been extensively used for this task. However, despite LSTM networks having proven useful in learning sequences, no methodology has been proposed for learning sequences conditional to constraints, such as given metrical structure or a given bass line. In this paper we examine the task of conditional rhythm generation of drum sequences with Neural Networks. The proposed network architecture is a combination of LSTM and feed forward (conditional) layers capable of learning long drum sequences, under constraints imposed by metrical rhythm information and a given bass sequence. The results indicate that the role of the conditional layer in the proposed architecture is crucial for creating diverse drum sequences under conditions concerning given metrical information and bass lines.

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Footnotes
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Panel Discussion in the ICMC’93.
 
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Metadata
Title
Combining LSTM and Feed Forward Neural Networks for Conditional Rhythm Composition
Authors
Dimos Makris
Maximos Kaliakatsos-Papakostas
Ioannis Karydis
Katia Lida Kermanidis
Copyright Year
2017
Book
Engineering Applications of Neural Networks

Print ISBN: 978-3-319-65171-2

Electronic ISBN: 978-3-319-65172-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-65172-9_48

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