English

Evaluating generative audio systems and their metrics

Sound 2022-09-02 v1 Machine Learning Audio and Speech Processing

Abstract

Recent years have seen considerable advances in audio synthesis with deep generative models. However, the state-of-the-art is very difficult to quantify; different studies often use different evaluation methodologies and different metrics when reporting results, making a direct comparison to other systems difficult if not impossible. Furthermore, the perceptual relevance and meaning of the reported metrics in most cases unknown, prohibiting any conclusive insights with respect to practical usability and audio quality. This paper presents a study that investigates state-of-the-art approaches side-by-side with (i) a set of previously proposed objective metrics for audio reconstruction, and with (ii) a listening study. The results indicate that currently used objective metrics are insufficient to describe the perceptual quality of current systems.

Keywords

Cite

@article{arxiv.2209.00130,
  title  = {Evaluating generative audio systems and their metrics},
  author = {Ashvala Vinay and Alexander Lerch},
  journal= {arXiv preprint arXiv:2209.00130},
  year   = {2022}
}

Comments

Accepted at ISMIR 2022

R2 v1 2026-06-28T00:31:34.724Z