Challenge on Sound Scene Synthesis: Evaluating Text-to-Audio Generation
This work addresses evaluation and controllability issues for researchers in text-to-audio generation, but it is incremental as it builds on existing methods with a new challenge framework.
The paper tackled challenges in controllability and evaluation for neural text-to-audio generation by organizing the Sound Scene Synthesis challenge, revealing that larger models generally performed better but lightweight approaches also showed promise, with a strong correlation between objective metrics and human ratings.
Despite significant advancements in neural text-to-audio generation, challenges persist in controllability and evaluation. This paper addresses these issues through the Sound Scene Synthesis challenge held as part of the Detection and Classification of Acoustic Scenes and Events 2024. We present an evaluation protocol combining objective metric, namely Fréchet Audio Distance, with perceptual assessments, utilizing a structured prompt format to enable diverse captions and effective evaluation. Our analysis reveals varying performance across sound categories and model architectures, with larger models generally excelling but innovative lightweight approaches also showing promise. The strong correlation between objective metrics and human ratings validates our evaluation approach. We discuss outcomes in terms of audio quality, controllability, and architectural considerations for text-to-audio synthesizers, providing direction for future research.