# TaikoNation: Patterning-focused Chart Generation for Rhythm Action Games

arXiv: https://arxiv.org/abs/2107.12506 · HTML: https://mlsanity.com/paper/2107.12506/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 44 references that are in the map.
- First posted on arXiv 26 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [Super Mario as a String: Platformer Level Generation Via LSTMs](https://mlsanity.com/paper/1603.00930/) (2016)
- [Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks](https://mlsanity.com/paper/2004.00151/) (2020)
- [Co-Creative Level Design via Machine Learning](https://mlsanity.com/paper/1809.09420/) (2018)
- [Sequential Segment-based Level Generation and Blending using Variational Autoencoders](https://mlsanity.com/paper/2007.08746/) (2020)
- [Dance Dance Convolution](https://mlsanity.com/paper/1703.06891/) (2017)
- [GenerationMania: Learning to Semantically Choreograph](https://mlsanity.com/paper/1806.11170/) (2018)

## Built on by (most cited first, 9 of 9)

- [Games for Artificial Intelligence Research: A Review and Perspectives](https://mlsanity.com/paper/2304.13269/) (2023)
- [Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AI](https://mlsanity.com/paper/2211.02759/) (2022)
- [Online Game Level Generation from Music](https://mlsanity.com/paper/2207.05271/) (2022)
- [Threshold Designer Adaptation: Improved Adaptation for Designers in Co-creative Systems](https://mlsanity.com/paper/2205.09269/) (2022)
- [Beat-Aligned Spectrogram-to-Sequence Generation of Rhythm-Game Charts](https://mlsanity.com/paper/2311.13687/) (2023)
- [Ordinal Regression for Difficulty Estimation of StepMania Levels](https://mlsanity.com/paper/2301.09485/) (2023)
- [Dance Dance ConvLSTM](https://mlsanity.com/paper/2507.01644/) (2025)
- [ChartGenEval: Corruption-Tested Multi-Dimensional Feedback for Rhythm-Game Chart Generation](https://mlsanity.com/paper/2607.12857/) (2026)
- [ITGPT: A Transformer Based Architecture for the Generation of Dance Dance Revolution and In the Groove Charts](https://mlsanity.com/paper/2607.14148/) (2026)

## Abstract (arXiv)

Generating rhythm game charts from songs via machine learning has been a problem of increasing interest in recent years. However, all existing systems struggle to replicate human-like patterning: the placement of game objects in relation to each other to form congruent patterns based on events in the song. Patterning is a key identifier of high quality rhythm game content, seen as a necessary component in human rankings. We establish a new approach for chart generation that produces charts with more congruent, human-like patterning than seen in prior work.