Bibliographic Details
| Title: |
Long-Form Text-to-Music Generation with Adaptive Prompts: A Case of Study in Tabletop Role-Playing Games Soundtracks |
| Authors: |
Marra, Felipe, Ferreira, Lucas N. |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Sound, Computer Science - Artificial Intelligence, Computer Science - Multimedia, Computer Science - Neural and Evolutionary Computing, Electrical Engineering and Systems Science - Audio and Speech Processing |
| More Details: |
This paper investigates the capabilities of text-to-audio music generation models in producing long-form music with prompts that change over time, focusing on soundtrack generation for Tabletop Role-Playing Games (TRPGs). We introduce Babel Bardo, a system that uses Large Language Models (LLMs) to transform speech transcriptions into music descriptions for controlling a text-to-music model. Four versions of Babel Bardo were compared in two TRPG campaigns: a baseline using direct speech transcriptions, and three LLM-based versions with varying approaches to music description generation. Evaluations considered audio quality, story alignment, and transition smoothness. Results indicate that detailed music descriptions improve audio quality while maintaining consistency across consecutive descriptions enhances story alignment and transition smoothness.
Comment: Paper accepted at the LAMIR 2024 workshop |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2411.03948 |
| Accession Number: |
edsarx.2411.03948 |
| Database: |
arXiv |
