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Evaluating generative audio systems and their metrics

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arxiv 2209.00130 v1 pith:O3Z4TUA6 submitted 2022-08-31 cs.SD cs.LGeess.AS

classification cs.SDcs.LGeess.AS
keywords metricsaudiodifferentsystemsdifficultgenerativeobjectiveperceptual
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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.

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Cited by 1 Pith paper

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  1. Benchmarking Music Generation Models and Metrics via Human Preference Studies

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A 15,600-comparison human study ranks 12 music generation models and finds that music-trained CLAP metrics correlate best with human preference.

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