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 | 2026-05-01

The Problem: AI That Can't Tell a Cathedral from a Bathroom

Current AI models can generate plausible sound effects, but they know almost nothing about spatial acoustics. They don't understand how a cathedral differs acoustically from a bathroom, and they cannot infer how sound should reflect based on a room's size, materials, or furniture visible in a video.

Spatial acoustics shape how we perceive the world:

• In a concert hall, a violin sounds warm and rich
• The same violin in a bathroom sounds harsh and thin
• Outdoors, there is almost no echo
• In a carpeted living room, sound is heavily absorbed

Our brains unconsciously use these acoustic cues to judge a space's size, materials, and even emotional atmosphere—but existing video-to-audio (V2A) models focus only on "what sound" while ignoring "what space the sound is in."

What MMAudioReverbs Does

The core insight: video contains rich spatial information—room size, wall materials, furniture layout—that can be used to infer how sound should propagate and reflect.

The system performs two tasks:

1. Dereverberation

• Input: heavily reverberant audio (e.g., speech recorded in a cave)
• Output: the "dry" signal with reverberation removed
• Key idea: use spatial cues from the video to distinguish direct sound from reflections

2. Room Impulse Response (RIR) Estimation

• Input: a video + audio clip
• Output: the room's "acoustic fingerprint"—its room impulse response
• With this fingerprint, any sound can be placed into that room

Applications

• Film post-production: blend ADR (re-recorded dialogue) into the actual scene's spatial acoustics
• Video conferencing: remove home-environment reverberation for clearer calls
• Game audio: generate acoustics matching the on-screen environment in real time
• Music production: make virtual instruments sound as if played in a real concert hall
• AR/VR: seamlessly blend virtual object sounds with the real environment

Key Takeaway: Space Is Part of the Sound

As the post puts it, echoing Feynman's view of waves: a wave is not just a vibration—it is vibration propagating through space. The same tuning fork produces entirely different listening experiences in a concert hall, a bathroom, or outdoors; the space itself, as part of the sound, changes what we perceive.

MMAudioReverbs argues that to truly understand and generate sound, AI must simultaneously understand the space the sound inhabits. Questions worth asking if you build audio or video generation systems:

1. Does your model account for spatial acoustics? 2. Can visual information help infer the acoustic environment? 3. Is generated sound consistent with the scene's spatial characteristics? 4. Can you separate the "source itself" from "spatial effects"?

In the era of multimodal AI, audio should not be isolated from video—spatial acoustics is the bridge connecting visual and auditory understanding.