ABSTRACT
This paper is aimed to investigate the discrepancy between different methods for music generation. We claim that comparing traditional and classical music generation algorithms, music generation methods using VAEs are more favored by the listener. In this paper, we meticulously present computer-based music generation approaches including the Markov chain, genetic algorithm, and deep learning methods. The number of parameters for the deep learning method is much larger than traditional optimization algorithms, which will optimize songs that are more aesthetically pleasing. Finally, we select twenty participants to compare the music generated by different methods, the test result shows that participants prefer more on the music generated by the VAEs.
- Cope, D., “Experiments in musical intelligence (EMI): Non‐linear linguistic‐based composition,” Interface 18(1–2), 117–139, 1989.Google ScholarCross Ref
- Michael, C., John, P. and Emery, S., “Improving algorithmic music composition with machine learning”, 2006.Google Scholar
- Özcan, E. and Erçal, T., “A Genetic Algorithm for Generating Improvised Music”, 2008.Google Scholar
- Guo, Q., “Computer-assisted music composition algorithm design dependent on interactive genetic algorithm with interval fitness,” J Phys Conf Ser 2066(1), IOP Publishing Ltd, 2021.Google Scholar
- Gale, E., Matthews, O., Costello, B. de L. and Adamatzky, A., “Beyond Markov Chains, Towards Adaptive Memristor Network-based Music Generation”, 2013.Google Scholar
- Shapiro, I. and Huber, M., “Markov Chains for Computer Music Generation,” Journal of Humanistic Mathematics 11(2), 167–195, 2021.Google ScholarCross Ref
- Zaky, H. and Aciek, I. W., “Music Generator with Markov Chain: a Case Study with Beatme Touchdown”, 2016.Google Scholar
- Yang, L.-C., Chou, S.-Y. and Yang, Y.-H., “MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation”, 2017.Google Scholar
- Trieu, N., College, H. M. and Keller, R. M., “JazzGAN: Improvising with Generative Adversarial Networks Grid Computing View project Intelligent Music Software View project JazzGAN: Improvising with Generative Adversarial Networks”, 2018.Google Scholar
- Yu, Y., Srivastava, A. and Canales, S., “Conditional LSTM-GAN for Melody Generation from Lyrics”, 2019.Google Scholar
- Boulanger-Lewandowski, N., Bengio, Y. and Vincent, P., “Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription,” Proceedings of the 29th International Conference on Machine Learning, ICML 2012 2, 1159–1166, 2012.Google Scholar
- Huang, S., Li, Q., Anil, C., Bao, X., Oore, S. and Grosse, R. B., “TimbreTron: A WaveNet (CycleGAN (CQT(Audio))) Pipeline for Musical Timbre Transfer”, 2018.Google Scholar
Index Terms
- A Comprehensive Study on Algorithm-Based Music Generation
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