MMAudioReverbs: Video-Guided Acoustic Modeling for Dereverberation and Room Impulse Response Estimation

Forum topic · 小凯 · 2026-05-04

Summary

MMAudioReverbs is a research paper (arXiv: 2605.00431) by Akira Takahashi, Ryosuke Sawata, Shusuke Takahashi, and Yuki Mitsufuji that addresses a key blind spot in video-to-audio (V2A) generation: room acoustics. Existing V2A models can generate semantically correct sounds (e.g., a drum hit when seeing a drum) but cannot model how reverberation varies with room size, materials, and furniture, leaving generated audio without a sense of spatial presence. The authors hypothesize that pretrained V2A models already encode implicit visual-to-acoustic mappings learned from video-audio training pairs, and that this knowledge can be extracted. The proposed method uses video guidance for two tasks: dereverberation (producing clean audio from reverberant input given a view of the room) and room impulse response (RIR) estimation (recovering a room's acoustic fingerprint from visuals). RIR estimates can then be used to place clean audio in a specific space or simulate how it would sound there. Applications include music production, video conferencing, VR/AR immersive audio, and architectural acoustics.

> Paper: MMAudioReverbs: Video-Guided Acoustic Modeling for Dereverberation and Room Impulse Response Estimation > Authors: Akira Takahashi, Ryosuke Sawata, Shusuke Takahashi, Yuki Mitsufuji > arXiv: 2605.00431 | 2026-04-29

The problem: AI that can't tell which room it's in

Imagine listening to a recording with some echo. Current video-to-audio (V2A) models can generate "semantically correct" sounds — see a drum, hear a drum — but they cannot model the room acoustics. The same drum hit sounds completely different in a large hall versus a small bathroom, and existing models lack control over reverberation. The result is audio that seems to "float in the air" with no sense of presence.

Why room acoustics matter

Reverberation — reflections of sound that create tail and echo — gives each room a distinct "acoustic fingerprint." Controlling or understanding it is important for:

Music production (recording environment control)
Video conferencing (removing room echo)
VR/AR (immersive audio requires correct acoustics)
Architectural acoustics (designing better concert halls)

Video-guided acoustic modeling

The paper's core hypothesis: V2A models implicitly know the relationship between spatial audio and visual cues — and we can extract that knowledge.

The approach:

1. Extract acoustic knowledge from a pretrained V2A model. Training video-audio pairs across many rooms encode a visual-to-acoustic mapping in the model weights. 2. Dereverberation. Input: reverberant audio plus video. The video provides room size and material information; output is clean, dry audio. 3. Room impulse response (RIR) estimation. From video alone, estimate the room's acoustic fingerprint. The estimated RIR can simulate how sound behaves in that room, or be used to place clean audio "inside" a specific space.

Why video can predict acoustics

Visual cues carry acoustic information:

Room size: a spacious hall implies long reverberation; a small bedroom implies short reverberation.
Materials: marble walls reflect strongly (bright reverb); carpets and curtains absorb (dull reverb).
Furniture: an empty room gives strong echo; a furnished room scatters and absorbs sound.

Takeaways

If you work on audio or video generation, ask yourself:

1. Does my audio model account for spatial acoustics? 2. Can visual information help audio processing? 3. How can implicit knowledge in pretrained models be extracted and reused?

MMAudioReverbs reminds us that good audio is not just the "right sound" — it is the right sound in the right space. When AI can "read" a room's acoustic properties from video, audio generation moves from flat to spatial, from abstract to present. In the world of sound, space is not a backdrop; it is part of the sound itself.

--- *Translated and edited from a Chinese forum post on zhichai.net.*