AdVerb: Visually Guided Audio Dereverberation
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Abstract (arXiv)
We present AdVerb, a novel audio-visual dereverberation framework that uses visual cues in addition to the reverberant sound to estimate clean audio. Although audio-only dereverberation is a well-studied problem, our approach incorporates the complementary visual modality to perform audio dereverberation. Given an image of the environment where the reverberated sound signal has been recorded, AdVerb employs a novel geometry-aware cross-modal transformer architecture that captures scene geometry and audio-visual cross-modal relationship to generate a complex ideal ratio mask, which, when applied to the reverberant audio predicts the clean sound. The effectiveness of our method is demonstrated through extensive quantitative and qualitative evaluations. Our approach significantly outperforms traditional audio-only and audio-visual baselines on three downstream tasks: speech enhancement, speech recognition, and speaker verification, with relative improvements in the range of 18% - 82% on the LibriSpeech test-clean set. We also achieve highly satisfactory RT60 error scores on the AVSpeech dataset.
Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 60 of its 95 references that are in the map. First posted on arXiv 23 Aug 2023.
Builds on
60 of 95 references.
References · 60
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- 2021AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesiscited by 339
- 2018Audio-Visual Scene Analysis with Self-Supervised Multisensory Featurescited by 335
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- 2024SEE-2-SOUND: Zero-Shot Spatial Environment-to-Spatial Sound6 Jun 2024
- 2024NeRAF: 3D Scene Infused Neural Radiance and Acoustic Fields28 May 2024
- 2024Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark27 Mar 2024
- 2023AV-RIR: Audio-Visual Room Impulse Response Estimation30 Nov 2023