An Attempt to Generate Mozart's Piano Compositions Based on an LSTM Model
DOI:
https://doi.org/10.62051/ijcsit.v4n1.13Keywords:
LSTM, Music Generation, Music21, Deep Learning, Neural Networks, Classical Piano Works, Model Training, Music Composition, Recurrent Neural NetworksAbstract
LSTM, proposed by German computer scientists Sepp Hochreiter and Jürgen Schmidhuber in 1997 [3], has shown outstanding performance in various tasks, especially in tasks that require capturing long-term dependencies. These tasks include language modeling and machine translation in the field of natural language processing, as well as stock price prediction. This project aims to explore the potential of LSTM models in the field of music generation by analyzing Mozart's classical piano works using music21 and creating a training dataset. The project uses LSTM to learn this dataset, adjusting the forget gate, input gate, and different training iteration times to generate diversified outputs. Finally, the generated music data was manually selected and labeled.
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[1] Huang, A. and Wu, R., “Deep Learning for Music”, arXiv e-prints, 2016. doi:10.48550/arXiv.1606.04930.
[2] Tang, J., Wiggins, G., and Fazekas, G., “Reconstructing Human Expressiveness in Piano Performances with a Transformer Network”, arXiv e-prints, 2023. doi:10.48550/arXiv.2306.06040.
[3] S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," in Neural Computation, vol. 9, no. 8, pp. 1735-1780, 15 Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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