Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 19, 08 December 2023
Open
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Using LSTM neural network to generate music
* Author to whom correspondence should be addressed.
Abstract
Music generation is a cutting edge and useful research filed, which is helpful for artists to compose novel melodies as well as revealing potential patterns of music. Recurrent neural network (RNN) is a member of the neural network family, which is commonly used for processing sequential data. It can deal with sequential changes in data compared to normal neural networks. Long short-term memory (LSTM) aims at improving the conventional RNN. It is designed to alleviate the deficiencies of gradient disappearance and gradient explosion that possibly happened in RNN during training. In simple terms, LSTM is superior at grasping long term information than normal RNN. It can record the information that requires to be recorded for a long time and abandon these unimportant features. Unlike RNN, which have merely one way of stacking long-term information. It's quite useful for tasks that require long range dependence. In this work the effectiveness of the LSTM is validated on the music generation task.
Keywords
LSTM, RNN, deep learning, music generation.
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-203-9
- ISBN (Online)
- 978-1-83558-204-6
- Published Date
- 08 December 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/19/20230512
- Copyright
- 08 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited