arxiv: 2605.00431 · v1 · submitted 2026-05-01 · 💻 cs.SD · cs.CV· cs.LG· eess.AS

Recognition: unknown

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

Akira Takahashi , Ryosuke Sawata , Shusuke Takahashi , Yuki Mitsufuji

Authors on Pith no claims yet

Pith reviewed 2026-05-09 18:58 UTC · model grok-4.3

classification 💻 cs.SD cs.CVcs.LGeess.AS

keywords video-to-audiodereverberationroom impulse responseacoustic modelingfine-tuningspatial audioroom acoustics

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The pith

A pretrained video-to-audio model can be fine-tuned to estimate room impulse responses and remove reverberation without any architectural changes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The authors argue that video-to-audio models already encode implicit links between visual scenes and how sound behaves in physical spaces. They demonstrate this by adapting one such model into a single system that both cleans reverberation from audio and predicts the room's acoustic fingerprint from video. The adaptation uses only fine-tuning on limited data and leaves the original network untouched. If the claim holds, existing synthesis models become ready-made tools for grounded acoustic analysis rather than requiring separate physics-based simulators. Results show that visual and audio signals each prove stronger for different aspects of room acoustics.

Core claim

MMAudioReverbs is a unified framework based on the pretrained MMAudio video-to-audio model that performs both dereverberation and room impulse response estimation without network architectural modification, achieved through fine-tuning on a small dataset. The approach rests on the premise that such models already carry semantic knowledge of how spatial audio relates to visual cues, allowing them to serve as priors for physically grounded room-acoustic processing.

What carries the argument

MMAudioReverbs, the fine-tuned MMAudio model repurposed as a prior that jointly handles dereverberation and room impulse response prediction from combined audio-visual input.

If this is right

Visual cues and audio cues each provide an advantage for different types of physical room acoustics.
No network redesign is required to turn a video-to-audio synthesis model into a room-acoustic analysis tool.
Foundation video-to-audio models become directly applicable to physically grounded room-acoustic tasks.
The same model can switch between removing echoes and predicting impulse responses depending on the input.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The method could let video platforms automatically adjust audio tracks to match the depicted room without manual re-recording.
It suggests a path toward estimating acoustic properties of real environments solely from video footage captured by ordinary cameras.
Similar adaptation might extend to other acoustic parameters such as absorption or early reflections if the implicit knowledge is sufficiently rich.

Load-bearing premise

That pretrained video-to-audio models already contain usable implicit knowledge about how visual scenes determine the acoustic behavior of sound in those spaces.

What would settle it

A controlled test in which fine-tuning the base model on the small dataset produces no measurable gain over a standard acoustic baseline or an untrained version when measured on standard dereverberation and room impulse response estimation benchmarks.

Figures

Figures reproduced from arXiv: 2605.00431 by Akira Takahashi, Ryosuke Sawata, Shusuke Takahashi, Yuki Mitsufuji.

**Figure 1.** Figure 1: Outline of MMAudioReverbs that can handle two view at source ↗

**Figure 2.** Figure 2: MMAudio backbone and its task-specific reinterpretation for dereverberation and RIR estimation. (a) MMAudio view at source ↗

**Figure 3.** Figure 3: Visualization examples of RIR estimation. Top: view at source ↗

read the original abstract

Although recent video-to-audio (V2A) models excelled at synthesizing semantically plausible sounds from visual inputs, they do not explicitly model room-acoustic effects such as reverberation or room impulse responses (RIRs), and thus offer limited controllability over these effects. However, we hypothesize that such V2A models implicitly have semantic knowledge of the relationship between spatial audio and the corresponding vision cues. In this paper, we revisit a V2A model for the sake of the above, and propose the way to utilize the pretrained model as prior for physically grounded room-acoustic processing. Based on one of the state-of-the-art V2A models, MMAudio, we propose MMAudioReverbs that is a unified framework dealing with i) dereverberation and ii) room impulse response (RIR) estimation without network architectural modification, and fine-tuned on a small dataset. Experimental results showed that audio and visual cues respectively have advantage depending on the type of physical room acoustics. It implies that foundation V2A models can be used for physically grounded room-acoustic analysis.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

MMAudioReverbs fine-tunes a pretrained V2A model for dereverberation and RIR estimation without architecture changes, but the results do not isolate whether pretraining supplies the claimed prior.

read the letter

The main thing to know is that this paper takes an existing video-to-audio model, MMAudio, and fine-tunes it on a small dataset to perform both dereverberation and room impulse response estimation in a single framework, keeping the network unchanged. It reports that audio cues and visual cues each show advantages for different aspects of room acoustics. That is the concrete output. The approach is straightforward and avoids the usual need to redesign the model for these physical tasks. The cue-advantage finding is a usable observation for anyone trying to add controllable reverberation to generated audio. The paper does a reasonable job of framing the problem and showing that the same model can be steered toward these outputs with limited extra data. The unified handling of two related acoustic tasks is cleaner than separate pipelines would be. The soft spot is the missing test for the central hypothesis. The authors assume the pretrained weights already encode useful spatial audio-visual relations, yet nothing in the description separates that from what the fine-tuning step itself learns. A from-scratch baseline or a frozen-backbone run would have clarified whether the gains come from the prior or from ordinary adaptation. The abstract mentions experimental results on cue advantages but supplies no numbers, baselines, or dataset statistics, so the strength of the claim cannot be judged from what is shown. If the full paper contains those details and they are clean, the work improves; if not, the practical utility stays limited. This is for people working on video-to-audio generation or acoustic modeling for virtual environments who want a quick way to inject room effects. A reader already familiar with V2A models could pick up the fine-tuning recipe and the cue observation. It is worth sending to peer review so the experiments can be checked for proper controls and metrics. The idea is simple enough that referees could quickly say whether the evidence supports the reuse claim.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes MMAudioReverbs, a unified framework built on the pretrained MMAudio video-to-audio (V2A) model to perform both dereverberation and room impulse response (RIR) estimation. The method fine-tunes the existing model on a small dataset without architectural changes, under the hypothesis that pretrained V2A models implicitly encode semantic knowledge of spatial audio-visual relationships. Experimental results are said to show that audio and visual cues offer complementary advantages depending on the type of physical room acoustics, implying that foundation V2A models can support physically grounded room-acoustic analysis.

Significance. If the central claims hold after appropriate controls, the work would illustrate an efficient route for adapting large pretrained multimodal models to acoustic modeling tasks using limited additional data, potentially improving controllability in video-guided audio synthesis and analysis.

major comments (2)

[Abstract] Abstract: the abstract asserts that 'audio and visual cues respectively have advantage depending on the type of physical room acoustics' and that the approach exploits implicit knowledge in the pretrained model, yet supplies no quantitative metrics, baselines, dataset details, or error analysis, rendering it impossible to evaluate whether the data support the cue-advantage or prior-exploitation claims.
[Proposed approach / Experiments] The central hypothesis (that performance derives from implicit semantic knowledge in the pretrained MMAudio weights rather than de-novo learning during fine-tuning) is load-bearing for the claim of using the model 'as prior' without architectural modification. No ablation isolating this contribution—such as a comparison of the fine-tuned pretrained model against an identical architecture trained from random initialization on the same small dataset—is described.

minor comments (2)

[Abstract] The description of the 'small dataset' used for fine-tuning lacks size, source, generation procedure, and train/validation/test splits, which are required to assess generalization and reproducibility.
[Method] The statement that the framework operates 'without network architectural modification' would benefit from an explicit statement of which layers or heads are updated during fine-tuning and which outputs are added for the two tasks.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We address each major comment point by point below. Where the comments identify areas for improvement, we have revised the manuscript accordingly to strengthen the presentation and evidence.

read point-by-point responses

Referee: [Abstract] Abstract: the abstract asserts that 'audio and visual cues respectively have advantage depending on the type of physical room acoustics' and that the approach exploits implicit knowledge in the pretrained model, yet supplies no quantitative metrics, baselines, dataset details, or error analysis, rendering it impossible to evaluate whether the data support the cue-advantage or prior-exploitation claims.

Authors: We agree that the abstract, as a concise summary, would benefit from additional specifics to better contextualize the claims. In the revised manuscript, we have expanded the abstract to include key quantitative results (such as dereverberation metrics like PESQ and STOI, and RIR estimation errors), a brief description of the dataset (simulated and real acoustic environments), and mention of the main baselines (audio-only and visual-only configurations). These additions allow readers to more readily evaluate the complementary advantages of the cues while maintaining the abstract's brevity. revision: yes
Referee: [Proposed approach / Experiments] The central hypothesis (that performance derives from implicit semantic knowledge in the pretrained MMAudio weights rather than de-novo learning during fine-tuning) is load-bearing for the claim of using the model 'as prior' without architectural modification. No ablation isolating this contribution—such as a comparison of the fine-tuned pretrained model against an identical architecture trained from random initialization on the same small dataset—is described.

Authors: We acknowledge that this ablation would provide direct evidence isolating the contribution of the pretrained weights. Training an identical model from random initialization on our small fine-tuning dataset would be expected to overfit and generalize poorly, but we recognize the value of the comparison. In the revised manuscript, we have added this ablation experiment (reported in a new table and section), which shows that the pretrained initialization yields substantially better performance on both tasks than random initialization. This supports our hypothesis that the model leverages implicit spatial audio-visual knowledge from pretraining rather than learning solely from the limited fine-tuning data. revision: yes

Circularity Check

0 steps flagged

No circularity: derivation relies on external pretrained V2A model plus empirical fine-tuning with no self-referential reductions

full rationale

The paper's chain begins with an explicit hypothesis about implicit knowledge in pretrained V2A models (MMAudio), then fine-tunes that fixed architecture on a small dataset for dereverberation and RIR estimation. No equations define outputs in terms of themselves, no fitted parameters are relabeled as predictions, and no load-bearing uniqueness theorem or ansatz is imported via self-citation. The central results are empirical performance numbers after fine-tuning; the hypothesis is stated as an assumption rather than derived from the paper's own data or definitions. This matches the default case of a self-contained empirical adaptation of an external model.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review performed from abstract only; full paper may contain additional fitted parameters or assumptions not visible here.

axioms (1)

domain assumption Pretrained V2A models implicitly encode semantic knowledge linking spatial audio properties to visual scene cues
Explicitly stated as the motivating hypothesis in the abstract.

pith-pipeline@v0.9.0 · 5513 in / 1210 out tokens · 31907 ms · 2026-05-09T18:58:42.245059+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

15 extracted references

[1]

C. Chen, R. Gao, P. Calamia, and K. Grauman. Visual acoustic matching. InCVPR, 2022. 3

2022
[2]

C. Chen, C. Schissler, S. Garg, P. Kobernik, A. Clegg, P. Calamia, D. Batra, P. W. Robinson, and K. Grauman. Soundspaces 2.0: A simulation platform for visual-acoustic learning. InNeurIPS, 2022. 3

2022
[3]

C. Chen, W. Sun, D. Harwath, and K. Grauman. Learning audio- visual dereverberation. InICASSP, 2023. 3

2023
[4]

Z. Chen, P. Seetharaman, B. Russell, O. Nieto, D. Bourgin, A. Owens, and J. Salamon. Video-guided foley sound generation with multimodal controls. InCVPR, 2025. 1, 2

2025
[5]

H. K. Cheng, M. Ishii, A. Hayakawa, T. Shibuya, A. Schwing, and Y . Mitsufuji. MMAudio: Taming multimodal joint training for high- quality video-to-audio synthesis. InCVPR, 2025. 1, 2

2025
[6]

S.-g. Lee, W. Ping, B. Ginsburg, B. Catanzaro, and S. Yoon. BigV- GAN: A universal neural vocoder with large-scale training. InICLR,
[7]

A. Luo, Y . Du, M. J. Tarr, J. B. Tenenbaum, A. Torralba, and C. Gan. Learning neural acoustic fields. InNeurIPS, 2022. 1, 2

2022
[8]

Majumder, C

S. Majumder, C. Chen, Z. Al-Halah, and K. Grauman. Few-shot audio-visual learning of environment acoustics. InNeurIPS, 2022. 1

2022
[9]

Nakatani, T

T. Nakatani, T. Yoshioka, K. Kinoshita, M. Miyoshi, and B.-H. Juang. Speech dereverberation based on variance-normalized de- layed linear prediction.IEEE Transactions on Audio, Speech, and Language Processing, 2010. 3

2010
[10]

Ratnarajah, I

A. Ratnarajah, I. Ananthabhotla, V . K. Ithapu, P. Hoffmann, D. Manocha, and P. Calamia. Towards improved room impulse re- sponse estimation for speech recognition. InICASSP, 2023. 3

2023
[11]

Ratnarajah, S

A. Ratnarajah, S. Ghosh, S. Kumar, P. Chiniya, and D. Manocha. A V-RIR: Audio-visual room impulse response estimation. InCVPR,
[12]

M. F. Saad and Z. Al-Halah. How would it sound? material- controlled multimodal acoustic profile generation for indoor scenes. InICCV, 2025. 2

2025
[13]

Singh, J

N. Singh, J. Mentch, J. Ng, M. Beveridge, and I. Drori. Im- age2Reverb: Cross-modal reverb impulse response synthesis. In ICCV, 2021. 2, 3

2021
[14]

C. J. Steinmetz, V . K. Ithapu, and P. Calamia. Filtered noise shaping for time domain room impulse response estimation from reverberant speech. InWASPAA, 2021. 3

2021
[15]

Takahashi, S

A. Takahashi, S. Takahashi, and Y . Mitsufuji. MMAudioSep: Taming video-to-audio generative model towards video/text-queried sound separation. InICASSP, 2026. 1

2026