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

> 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: Audio AI That Can't Tell a Cathedral from a Bathroom

Imagine listening to a recording with some echo, but the AI cannot tell you whether it was made in a cathedral or a bathroom. Current video-to-audio (V2A) models can generate *semantically correct* sounds — see a drum, generate a drum hit — but they fail to model *room acoustics*. The same drum sounds completely different in a large hall versus a small room, and existing models lack control over reverberation.

Room Acoustics: The Overlooked Dimension

What is reverberation?

• Sound reflects off surfaces in a room, producing tails and echoes
• Different rooms have distinct "acoustic fingerprints"

Why it matters:

• Music production: control over the recording environment
• Video conferencing: removing room echo
• VR/AR: immersive audio requires correct acoustics
• Architectural acoustics: designing better concert halls

The blind spot of existing V2A models: they learn *what* sound to produce, but not *in what space*, resulting in audio that seems to float in the air with no sense of presence.

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.

Technical approach:

1. Extract acoustic knowledge from a V2A model — the pretrained model has seen countless video-audio pairs, and the visual-to-acoustic mapping is encoded in its weights. 2. Dereverberation — given reverberant audio plus video (which provides room size and material cues), output clean, dry audio. 3. Room Impulse Response (RIR) estimation — estimate a room's acoustic fingerprint from video alone, enabling simulation of how any clean audio would sound in that room.

It works like an acoustic engineer who glances at a room — its size, materials, and furniture — and can predict how sound will behave inside it.

Why Can Video 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)
• Furnishings: empty rooms produce strong echoes; furnished rooms scatter and absorb sound

Because training data contains video-audio pairs from many rooms, the model implicitly encodes this visual-to-acoustic mapping — we just need to unlock it.

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*, but the *right sound in the right space*. When AI learns to "read" a room's acoustic properties from video, audio generation moves from flat to three-dimensional, and from abstract to present. In the world of sound, space is not the background — it is part of the sound itself.