# Leveraging AI to Generate Audio for User-generated Content in Video Games

arXiv: https://arxiv.org/abs/2404.17018 · HTML: https://mlsanity.com/paper/2404.17018/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [A Two-stage Complex Network using Cycle-consistent Generative Adversarial Networks for Speech Enhancement](https://mlsanity.com/paper/2109.02011/) (2021)

## Built on by (most cited first, 1 of 1)

- [AudioTurbo: Fast Text-to-Audio Generation with Rectified Diffusion](https://mlsanity.com/paper/2505.22106/) (2025)

## Abstract (arXiv)

In video game design, audio (both environmental background music and object sound effects) play a critical role. Sounds are typically pre-created assets designed for specific locations or objects in a game. However, user-generated content is becoming increasingly popular in modern games (e.g. building custom environments or crafting unique objects). Since the possibilities are virtually limitless, it is impossible for game creators to pre-create audio for user-generated content. We explore the use of generative artificial intelligence to create music and sound effects on-the-fly based on user-generated content. We investigate two avenues for audio generation: 1) text-to-audio: using a text description of user-generated content as input to the audio generator, and 2) image-to-audio: using a rendering of the created environment or object as input to an image-to-text generator, then piping the resulting text description into the audio generator. In this paper we discuss ethical implications of using generative artificial intelligence for user-generated content and highlight two prototype games where audio is generated for user-created environments and objects.