The Application of LSTM in the AI-Based Enhancement of Classical Compositions
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Abstract
Music enhancement through deep learning methodologies presents an innovative approach to refining and augmenting classical compositions. Leveraging a comprehensive dataset of classical piano MIDI files, this study employs LSTM networks with attention mechanisms for music refinement. The model, trained on diverse compositions, demonstrates proficiency in capturing tempo nuances but faces challenges in replicating varied pitch patterns. Assessments by 28 individuals reveal positive reception, particularly in melody integration, scoring notably high at 8 out of 10. However, while praised for cohesion, bass lines received slightly lower scores, suggesting opportunities for enhancing originality and impact. These findings underscore the LSTM model's capability to generate harmonious melodies and highlight refinement areas, particularly in innovating bass lines within classical compositions. This study contributes to advancing automated music refinement, guiding further developments in LSTM-based music generation techniques.
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How to Cite
Fudholi, D., Putri, D., Wijaya, R., Kusnadi, J., & Amarissa, J. (2024). The Application of LSTM in the AI-Based Enhancement of Classical Compositions. Journal of Informatics Information System Software Engineering and Applications (INISTA), 7(1), 107-117. https://doi.org/10.20895/inista.v7i1.1628
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[6] V. Velardo and M. Vallati, "Automated Planning and Music Composition: an Efficient Approach for Generating Musical Structures," in Proceedings of the 11th International Symposium on Computer Music Multidisciplinary Research (CMMR 2015), Plymouth, UK: Springer, 2015, pp. 778–790.
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[12] L. S. Nistal J. and G. Richard, "Comparing Representations for Audio Synthesis Using Generative Adversarial Networks," in 2020 28th European Signal Processing Conference (EUSIPCO), Jan. 2021, pp. 161–165. doi: 10.23919/Eusipco47968.2020.9287799.
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[14] C. S.-P. Wan C.-H. and H.-Y. Lee, "Towards Audio to Scene Image Synthesis Using Generative Adversarial Network," in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019, pp. 496–500. doi: 10.1109/ICASSP.2019.8682383.
[15] D. S. Sajad S. and M. Meleet, "Music Generation for Novices Using Recurrent Neural Network (RNN)," in 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Sep. 2021, pp. 1–6. doi: 10.1109/ICSES52305.2021.9633906.
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