# The NES Video-Music Database: A Dataset of Symbolic Video Game Music Paired with Gameplay Videos

arXiv: https://arxiv.org/abs/2404.04420 · HTML: https://mlsanity.com/paper/2404.04420/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 5 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment](https://mlsanity.com/paper/1709.06298/) (2017)
- [MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training](https://mlsanity.com/paper/2106.05630/) (2021)
- [Video Background Music Generation with Controllable Music Transformer](https://mlsanity.com/paper/2111.08380/) (2021)
- [Audeo: Audio Generation for a Silent Performance Video](https://mlsanity.com/paper/2006.14348/) (2020)
- [Learning Interpretable Representation for Controllable Polyphonic Music Generation](https://mlsanity.com/paper/2008.07122/) (2020)
- [MusPy: A Toolkit for Symbolic Music Generation](https://mlsanity.com/paper/2008.01951/) (2020)
- [Learning to Generate Music With Sentiment](https://mlsanity.com/paper/2103.06125/) (2021)
- [Video Background Music Generation: Dataset, Method and Evaluation](https://mlsanity.com/paper/2211.11248/) (2022)
- [Computer-Generated Music for Tabletop Role-Playing Games](https://mlsanity.com/paper/2008.07009/) (2020)
- [Multi-Instrumentalist Net: Unsupervised Generation of Music from Body Movements](https://mlsanity.com/paper/2012.03478/) (2020)
- [The NES Music Database: A multi-instrumental dataset with expressive performance attributes](https://mlsanity.com/paper/1806.04278/) (2018)
- [Controlling Perceived Emotion in Symbolic Music Generation with Monte Carlo Tree Search](https://mlsanity.com/paper/2208.05162/) (2022)

## Built on by (most cited first, 4 of 4)

- [A Survey on Music Generation from Single-Modal, Cross-Modal, and Multi-Modal Perspectives](https://mlsanity.com/paper/2504.00837/) (2025)
- [Diffusion-based Symbolic Music Generation with Structured State Space Models](https://mlsanity.com/paper/2507.20128/) (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)
- [Video-to-Music Generation for Gameplay Videos](https://mlsanity.com/paper/2609.31810/) (2026)

## Abstract (arXiv)

Neural models are one of the most popular approaches for music generation, yet there aren't standard large datasets tailored for learning music directly from game data. To address this research gap, we introduce a novel dataset named NES-VMDB, containing 98,940 gameplay videos from 389 NES games, each paired with its original soundtrack in symbolic format (MIDI). NES-VMDB is built upon the Nintendo Entertainment System Music Database (NES-MDB), encompassing 5,278 music pieces from 397 NES games. Our approach involves collecting long-play videos for 389 games of the original dataset, slicing them into 15-second-long clips, and extracting the audio from each clip. Subsequently, we apply an audio fingerprinting algorithm (similar to Shazam) to automatically identify the corresponding piece in the NES-MDB dataset. Additionally, we introduce a baseline method based on the Controllable Music Transformer to generate NES music conditioned on gameplay clips. We evaluated this approach with objective metrics, and the results showed that the conditional CMT improves musical structural quality when compared to its unconditional counterpart. Moreover, we used a neural classifier to predict the game genre of the generated pieces. Results showed that the CMT generator can learn correlations between gameplay videos and game genres, but further research has to be conducted to achieve human-level performance.