Procedural Content Generation of Rhythm Games Using Deep Learning Methods
The rhythm game is a type of video game which is popular to many people. But the game contents (required action and its timing) of rhythm game are usually hand-crafted by human designers. In this research, we proposed an automatic generation method to generate game contents from the music file of the famous rhythm game “OSU!” 4k mode. Generally, the supervised learning method is used to generate such game contents. In this research some new methods are purposed, one is called “fuzzy label” method, which shows better performance on our training data. Another is to use the new model C-BLSTM. On our test data, we improved the F-Score of timestamp prediction from 0.8159 to 0.8430. Also, it was confirmed through experiments that human players could feel the generated beatmap is more natural than previous research.
KeywordsProcedural Content Generation Rhythm game C-BLSTM
This research is financially supported by Japan Society for the Promotion of Science (JSPS) under contract number 17K00506.
- 1.Osu! https://osu.ppy.sh/home. Accessed June 2017
- 3.Donahue, C., Lipton, Z.C., McAuley, J.: Dance dance convolution. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1039–1048. JMLR. org (2017)Google Scholar
- 4.Hamel, P., Bengio, Y., Eck, D.: Building musically-relevant audio features through multiple timescale representations (2012)Google Scholar
- 6.Kalchbrenner, N., Danihelka, I., Graves, A.: Grid long short-term memory. arXiv preprint arXiv:1507.01526 (2015)
- 7.Parascandolo, G., Huttunen, H., Virtanen, T.: Recurrent neural networks for polyphonic sound event detection in real life recordings. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6440–6444. IEEE (2016)Google Scholar
- 8.Pasinski, A.: Possible benefits of playing music video games (2014)Google Scholar
- 10.Schlüter, J., Böck, S.: Improved musical onset detection with convolutional neural networks. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6979–6983. IEEE (2014)Google Scholar