Dance Dance Gradation: A Generation of Fine-Tuned Dance Charts
This paper proposes a system to automatically generate dance charts with fine-tuned difficulty levels: Dance Dance Gradation (DDG). The system learns the relationships between difficult and easy charts based on the deep neural network using a dataset of dance charts with different difficulty levels as the training data. The difficulty chart automatically would be adapted to easier charts through the learned model. As mixing multiple difficulty levels for the training data, the generated charts should have each characteristic of difficulty level. The user can obtain the charts with intermediate difficulty level between two different levels. Through the objective evaluation and the discussions for the output results, it was suggested that the proposed system generated the charts with each characteristic of the difficulty level in the training dataset.
KeywordsRhythm-based video games Procedural content generation Difficulty Adjustment
This paper was supported in part by JSPS Grant-in-Aid for Young Scientists (B) #16K21482.
- 1.Andrade, G., Ramalho, G., Santana, H.S., Corruble, V.: Challenge-sensitive action selection: an application to game balancing. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pp. 194–200 (2005)Google Scholar
- 2.Donahue, C., Lipton, Z.C., McAuley, J.: Dance dance convolution. In: Proceedings of ICML 2017, pp. 1039–1048 (2017)Google Scholar
- 3.Hastings, E.J., Guha, R.K., Stanley, K.O.: Evolving content in the galactic arms race video game. In: Proceedings of CIG 2009, pp. 241–248 (2009)Google Scholar
- 4.Hunicke, R.: The case for dynamic difficulty adjustment in games. In: Advances in Computer Entertainment Technology, pp. 429–433 (2005)Google Scholar
- 5.Nakamura, E., Sagayama, S.: Automatic piano reduction from ensemble scores based on merged-output hidden markov model. In: Proceedings of ICMC 2015, pp. 298–305 (2015)Google Scholar
- 8.Yazawa, K., Itoyama, K., Okuno, H.G.: Automatic transcription of guitar tablature from audio signals in accordance with player’s proficiency. In: Proceedings of ICASSP 2014, pp. 3122–3126 (2014)Google Scholar