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InvFlowFD introduces a novel metric for evaluating generative music models by leveraging Flow Matching inversion, effectively removing the dependency on background datasets required by previous reference-free methods. This advancement allows for more flexible and accurate quality estimation that aligns closely with human perceptual judgments of audio distortions without needing paired noisy-clean data or aggregated statistics from external samples. The method represents a significant step forward in automated evaluation tools specifically designed for AI-generated audio content.
InvFlowFD: Reference-Free and Background-Set-Free Perceptual Music Quality Metric with Flow Matching Inversion
arXiv:2608.04142v1 Announce Type: cross Abstract: Existing reference-free methods for evaluating music perceptual quality alleviate the need for paired noisy-clean data, but they still rely on a background set, which is used to compute aggregated statistics of clean audio samples. In this work, we propose a novel approach that eliminates this requirement, achieving background-set-free and reference-free quality estimation using only a pre-trained Flow Matching backbone. We demonstrate that unconditional Flow Matching inversion via simple Euler integration is sufficient to detect various artificial distortions and accurately rank music generation models against human perceptual judgments. We introduce InvFlowFD, which performs flow inversion and compares a group of inverted samples to the prior distribution. We evaluate our method against prior work, quantitatively and with a thorough human study. Results suggest that InvFlowFD is highly correlated with human perception of sound distortions, as well as generative models' quality, while being more flexible and less restrictive than existing metrics.
Abstract:Existing reference-free methods for evaluating music perceptual quality alleviate the need for paired noisy-clean data, but they still rely on a background set, which is used to compute aggregated statistics of clean audio samples. In this work, we propose a novel approach that eliminates this requirement, achieving background-set-free and reference-free quality estimation using only a pre-trained Flow Matching backbone. We demonstrate that unconditional Flow Matching inversion via simple Euler integration is sufficient to detect various artificial distortions and accurately rank music generation models against human perceptual judgments. We introduce InvFlowFD, which performs flow inversion and compares a group of inverted samples to the prior distribution. We evaluate our method against prior work, quantitatively and with a thorough human study. Results suggest that InvFlowFD is highly correlated with human perception of sound distortions, as well as generative models' quality, while being more flexible and less restrictive than existing metrics.
Sound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2608.04142 [cs.SD]
(or arXiv:2608.04142v1 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2608.04142 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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