# Computer-Generated Music for Tabletop Role-Playing Games

arXiv: https://arxiv.org/abs/2008.07009 · HTML: https://mlsanity.com/paper/2008.07009/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2020.

## Built on by (most cited first, 23 of 23)

- [Games for Artificial Intelligence Research: A Review and Perspectives](https://mlsanity.com/paper/2304.13269/) (2023)
- [Controlling Perceived Emotion in Symbolic Music Generation with Monte Carlo Tree Search](https://mlsanity.com/paper/2208.05162/) (2022)
- [Generating music with sentiment using Transformer-GANs](https://mlsanity.com/paper/2212.11134/) (2022)
- [GPT for Games: A Scoping Review (2020-2023)](https://mlsanity.com/paper/2404.17794/) (2024)
- [GPT for Games: An Updated Scoping Review (2020-2024)](https://mlsanity.com/paper/2411.00308/) (2024)
- [AI-Based Affective Music Generation Systems: A Review of Methods, and Challenges](https://mlsanity.com/paper/2301.06890/) (2023)
- [ComMU: Dataset for Combinatorial Music Generation](https://mlsanity.com/paper/2211.09385/) (2022)
- [ABC-Eval: Benchmarking Large Language Models on Symbolic Music Understanding and Instruction Following](https://mlsanity.com/paper/2509.23350/) (2025)
- [EmoGen: Eliminating Subjective Bias in Emotional Music Generation](https://mlsanity.com/paper/2307.01229/) (2023)
- [N-Gram Unsupervised Compoundation and Feature Injection for Better Symbolic Music Understanding](https://mlsanity.com/paper/2312.08931/) (2023)
- [Emotion-driven Piano Music Generation via Two-stage Disentanglement and Functional Representation](https://mlsanity.com/paper/2407.20955/) (2024)
- [The NES Video-Music Database: A Dataset of Symbolic Video Game Music Paired with Gameplay Videos](https://mlsanity.com/paper/2404.04420/) (2024)
- [A Novel Multi-Task Learning Method for Symbolic Music Emotion Recognition](https://mlsanity.com/paper/2201.05782/) (2022)
- [Quantifying the Corpus Bias Problem in Automatic Music Transcription Systems](https://mlsanity.com/paper/2408.04737/) (2024)
- [Overhearing LLM Agents: A Survey, Taxonomy, and Roadmap](https://mlsanity.com/paper/2509.16325/) (2025)
- [Emotion-Conditioned Melody Harmonization with Hierarchical Variational Autoencoder](https://mlsanity.com/paper/2306.03718/) (2023)
- [MusER: Musical Element-Based Regularization for Generating Symbolic Music with Emotion](https://mlsanity.com/paper/2312.10307/) (2023)
- [Sound and Music Biases in Deep Music Transcription Models: A Systematic Analysis](https://mlsanity.com/paper/2512.14602/) (2025)
- [Long-Form Text-to-Music Generation with Adaptive Prompts: A Case Study in Tabletop Role-Playing Games Soundtracks](https://mlsanity.com/paper/2411.03948/) (2024)
- [First Steps Towards Overhearing LLM Agents: A Case Study With Dungeons & Dragons Gameplay](https://mlsanity.com/paper/2505.22809/) (2025)
- [Explicit Tonal Tension Conditioning via Dual-Level Beam Search for Symbolic Music Generation](https://mlsanity.com/paper/2511.19342/) (2025)
- [Story2MIDI: Emotionally Aligned Music Generation from Text](https://mlsanity.com/paper/2512.02192/) (2025)
- [Let the Model Learn to Feel: Mode-Guided Tonality Injection for Symbolic Music Emotion Recognition](https://mlsanity.com/paper/2512.17946/) (2025)

## Abstract (arXiv)

In this paper we present Bardo Composer, a system to generate background music for tabletop role-playing games. Bardo Composer uses a speech recognition system to translate player speech into text, which is classified according to a model of emotion. Bardo Composer then uses Stochastic Bi-Objective Beam Search, a variant of Stochastic Beam Search that we introduce in this paper, with a neural model to generate musical pieces conveying the desired emotion. We performed a user study with 116 participants to evaluate whether people are able to correctly identify the emotion conveyed in the pieces generated by the system. In our study we used pieces generated for Call of the Wild, a Dungeons and Dragons campaign available on YouTube. Our results show that human subjects could correctly identify the emotion of the generated music pieces as accurately as they were able to identify the emotion of pieces written by humans.