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arXiv · 2509.17219

Virtual Consistency for Audio Editing

Matthieu Cervera·Francesco Paissan·Mirco Ravanelli·Cem Subakan
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Abstract

Free-form, text-based audio editing remains a persistent challenge, despite progress in inversion-based neural methods. Current approaches rely on slow inversion procedures, limiting their practicality. We present a virtual-consistency based audio editing system that bypasses inversion by adapting the sampling process of diffusion models. Our pipeline is model-agnostic, requiring no fine-tuning or architectural changes, and achieves substantial speed-ups over recent neural editing baselines. Crucially, it achieves this efficiency without compromising quality, as demonstrated by quantitative benchmarks and a user study involving 16 participants.

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Matthieu Cervera, Francesco Paissan, Mirco Ravanelli, Cem Subakan. 2025-09-21. Virtual Consistency for Audio Editing. https://arxiv.org/abs/2509.17219

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