LD-LAudio-V1: Video-to-Long-Form-Audio Generation Extension with Dual Lightweight Adapters

Chaofan Ding; Haomin Zhang; Kristin Qi; Shuxin Yang; Xinhan Di; Zihao Chen

REVIEW 3 major objections 3 minor 22 references

LD-LAudio-V1: Video-to-Long-Form-Audio Generation Extension with Dual Lightweight Adapters

T0 review · 3 major / 3 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read LD-LAudio-V1 claims that adding two lightweight adapters to an existing video-to-audio model yields long-form audio with far fewer splicing artifacts and tighter temporal consistency than fine-tuning on short clips, while keeping…

desk verdict Plausible long-form audio contribution, but the only inspectable evidence is an abstract of point estimates; the full text is corrupted, so the claims are unverified. read the letter →

arxiv 2508.11074 v1 pith:Y2CALC6B submitted 2025-08-14 cs.SD cs.AIcs.CVeess.AS

Haomin Zhang , Kristin Qi , Shuxin Yang , Zihao Chen , Chaofan Ding , Xinhan Di This is my paper · ORCID

classification cs.SDcs.AIcs.CVeess.AS

keywords video-to-audiogenerationlong-formaudiolightweightadapterstemporalconsistencysplicingartifactssoundeffectsdatasetaudio-visualalignmentcomputationalefficiency

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tries to establish that long-form video-to-audio generation can be built as an extension of an existing short-form model rather than as a new model trained on long, noisy videos. It claims that two lightweight adapters, small trainable modules added to a frozen base model, remove the splicing artifacts and temporal inconsistencies that appear when the base model is fine-tuned on short clips. The method reports improvements across ten metrics, including a drop in FD_vgg from 3.75 to 1.28 and in the 10 ms energy delta from 0.3013 to 0.1349. The paper also releases a clean, human-annotated dataset of pure sound effects to support further work in this area.

What carries the argument

The central object is the pair of lightweight adapters inserted into a pretrained, frozen video-to-audio backbone. The adapters are described as small trainable modules that let the model produce long-form audio while preserving the base model's short-form knowledge and adding few parameters. The other load-bearing component is the released dataset: clean, human-annotated video-to-audio pairs containing pure sound effects without noise or artifacts, which is used to support training and evaluation for long-form generation.

What would settle it

Run LD-LAudio-V1 on a held-out test set of real film or television clips that mix speech, music, and effects, and compare FD_vgg, KL_panns, and the 10 ms energy delta against the original mixed audio; if the adapter advantages shrink or reverse, the pure-effect dataset premise is the load-bearing factor.

Watch

Extended reading notes

Core claim

The central claim is that dual lightweight adapters enable a state-of-the-art short-form video-to-audio model to generate long-form audio with materially higher quality than direct fine-tuning on short training videos. On the reported evaluation, the adapters improve every metric: FD_passt 450.00 to 327.29, FD_panns 34.88 to 22.68, FD_vgg 3.75 to 1.28, KL_panns 2.49 to 2.07, KL_passt 1.78 to 1.53, IS_panns 4.17 to 4.30, IB_score 0.25 to 0.28, the 10 ms energy delta 0.3013 to 0.1349, the 10 ms energy delta against ground truth 0.0531 to 0.0288, and semantic relevance 2.73 to 3.28. The paper interprets these gains as reduced splicing artifacts, better temporal consistency, and maintained computational efficiency while generating audio from video.

Load-bearing premise

The released dataset of clean, isolated, human-annotated sound effects represents the real distribution of video-to-audio content; if real footage contains mixed speech, music, and noise, the reported gains may not persist.

Editorial extensions

If this is right

Existing short-clip video-to-audio models can be upgraded to long-form generation by adding the adapter modules, leaving the base model frozen and keeping training cost low.
Long-form output should show fewer audible discontinuities at segment boundaries and steadier short-timescale loudness, since the 10 ms energy delta drops by more than half.
The released clean, human-annotated dataset gives the subfield a shared training and evaluation resource for long-form video-to-audio work.
The reported semantic-relevance gain, from 2.73 to 3.28, implies generated audio aligns more closely with on-screen events, which directly matters for post-production sound editing.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because the training and evaluation data are pure sound effects, the gains may not transfer to real footage that mixes speech, music, and incidental noise; a mixed-content benchmark would test this directly.
The same two-adapter recipe could plausibly be applied to other frozen generative backbones wherever temporal continuity is the bottleneck, not just video-to-audio.
The large improvement in the 10 ms energy delta suggests that the remaining artifact is concentrated at splice seams; a listening study on perceived continuity would complement the reported embedding distances.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things to know. First, the only readable evidence here is the abstract; the supplied PDF text is mojibake, with an unrelated astro-ph watermark, so no one can check the method, tables, or references. Second, on that abstract alone, the paper is a plausible but unproven contribution: dual lightweight adapters to turn a short-form video-to-audio model into a long-form one, plus a clean human-annotated SFX dataset. If real, this is useful for post-production and gives the subfield a shared benchmark.

What the paper does well: it identifies a real gap (long-form audio beyond ~10s, and noisy training data) and proposes a parameter-efficient fix instead of full long-video fine-tuning. The reported metric improvements are all in the same direction, which is encouraging, and the dataset release is the kind of contribution that often has value independent of the method.

The soft spots are the ones you'd expect from an abstract-only review. Every number in the results list is a point estimate with no error bars, no sample size, and no significance test. The baseline is self-defined (direct fine-tuning on short videos) and the base model is unnamed, so I can't tell how strong the comparison is. There is no perceptual evaluation, and 'reduces splicing artifacts and temporal inconsistencies' is asserted rather than demonstrated. The clean-dataset representativeness worry is real: if the target use-case is real video with speech and noise, training and evaluating on pure sound effects could overstate transfer. That concern is secondary, though, to the absence of methodological detail.

The watermark is the most concrete problem. I don't want to penalize the science for a corrupt PDF, but it means the manuscript can't be refereed in its current state. My recommendation: ask the authors for a clean full text, then send it to a competent referee. The idea deserves the time; the numbers do not yet earn acceptance.

I would not cite this until the details check out, and I wouldn't waste a reading group on a corrupted file.

Referee Report

3 major / 3 minor

Summary. The paper proposes LD-LAudio-V1, an extension of an unnamed state-of-the-art video-to-audio model with dual lightweight adapters for long-form audio generation, and releases a clean, human-annotated dataset of pure sound effects. The abstract reports large improvements over direct fine-tuning with short training videos across FD, KL, IS, energy-delta, and semantic-relevance metrics. However, the supplied full text is encoding-corrupted and contains an unrelated astro-ph watermark, so the method, experiments, tables, and references cannot be inspected.

Significance. If the claimed results hold, the contribution would be practically useful: long-form video-to-audio generation is an active bottleneck, and a clean dataset plus lightweight adapters is a reasonable direction. The authors deserve credit for releasing a dataset URL and for evaluating with multiple embedding-based metrics together with a temporal energy-consistency measure. As submitted, however, none of the headline claims can be verified: there is no named base model, no training/evaluation description, no variance or significance information, no perceptual evaluation, and no readable method text. The significance is therefore conditional and currently unsupported.

major comments (3)

[Full text (supplied)] The supplied manuscript text is not readable: it is encoding-garbled and carries the line 'arXiv:2508.11077v2 [astro-ph.SR] 20 Aug 2025', which is unrelated to this paper. Without a readable methods section, the dual-adapter architecture, the training recipe, the inference/stitching scheme, and the metric implementations cannot be checked. This is load-bearing because the central claim is that the two adapters, rather than confounding differences in training length, inference stitching, or evaluation set, drive the reported gains.
[Abstract] All ten headline metrics are point estimates with no sample sizes, confidence intervals, or significance tests, and the only baseline is described as 'direct fine-tuning with short training videos' without naming the base model, training duration, optimizer, or other hyperparameters. The word 'significant' is therefore not supported. Please report N, error bars or significance tests, and the exact baseline configuration, and include a perceptual or artifact-specific evaluation to back the claim about splicing artifacts and temporal inconsistencies.
[Abstract / dataset release] The evaluation appears to be performed on the released clean dataset of pure sound effects, but real video-to-audio inputs often contain speech, music, and noise. The representativeness of this distribution is not established, so the improvements may not transfer to realistic footage. Provide dataset statistics (duration, number of clips, label distribution, annotation protocol) and, if possible, evaluate on realistic or noisy videos to bound the transfer of the reported gains.

minor comments (3)

[Abstract] There is a typo in the abstract: 'zsynthesis' should be 'synthesis'.
[Abstract] The metrics FD_passt, FD_panns, FD_vgg, KL_panns, KL_passt, IS_panns, IB_score, and Sem.Rel. should be defined at first use, and it should be stated explicitly which metrics are higher-better versus lower-better.
[Abstract] The phrase 'extension of state-of-the-art video-to-audio models' is vague; the specific base model should be named so that readers can reproduce the comparison.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular step can be exhibited; reported gains are measured against a direct fine-tuning baseline.

full rationale

The accessible abstract reports relative improvements over a baseline described as "direct fine-tuning with short training videos" on embedding-based Frechet and KL distances, an inception score, an image-binding score, energy-delta statistics, and a semantic-relevance score. These metrics are computed on the released dataset, but they are not functions of the method's own fitted values; the baseline is an independent training variant rather than a parameter of the proposed model being renamed as a prediction. No equation, fitted parameter, or self-citation chain is visible in the corrupted full text that would allow a specific reduction of a claimed prediction to its own input. The concern that a clean, human-annotated dataset of pure sound effects may not be representative of realistic video-to-audio content is an empirical distributional premise, not a circular derivation. Therefore, per the rule that circularity must be exhibited with quoted evidence and a specific reduction, the honest finding is no significant circularity.

Assumptions & free parameters 2 free parameters · 3 assumptions · 1 invented entities

The central claim rests on the existence of a base video-to-audio model that can be extended by adapters (unnamed in the abstract), on the representativeness of the clean sound-effect dataset, and on the validity of embedding-based quality metrics. No equations are available for audit because the full text was unreadable.

free parameters (2)

Adapter hyperparameters (rank, insertion depth, capacity)
The abstract describes 'dual lightweight adapters' but states no values or design details; hand-chosen adapter settings would be free parameters on which the reported gains depend.
Training and evaluation duration split
The abstract contrasts with 'short training videos' and claims long-form capability but never states the clip durations used for training or evaluation; the definition of long-form is a free modeling choice.

assumptions (3)

domain assumption Visual content alone is sufficient to predict temporally synchronized audio.
The whole task presupposes this; the abstract states the goal without justifying the information-theoretic premise.
domain assumption Embedding-based Frechet distances, KL divergences, and 10 ms energy deltas are faithful proxies for perceived long-form audio quality and splicing artifacts.
All headline gains rest on these proxies; no perceptual evaluation is mentioned.
domain assumption The released clean dataset of pure sound effects without noise or artifacts is representative of real video-to-audio use cases.
If real videos contain mixed audio, speech, and noise, gains measured on clean effects may not transfer; no distributional analysis is available.

invented entities (1)

Dual lightweight adapters
purpose: Extend an existing video-to-audio backbone to long-form generation while avoiding full long-video fine-tuning.
These are architectural components introduced by the paper; the abstract offers only the paper's own metrics as support, with no external validation or independent falsifiable handle.

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Cite this review

Pith. "Pith review of LD-LAudio-V1: Video-to-Long-Form-Audio Generation Extension with Dual Lightweight Adapters." pith.science (2026). https://pith.science/paper/Y2CALC6B

@misc{pith2026250811074,
  author       = {Pith},
  title        = {Pith review of: LD-LAudio-V1: Video-to-Long-Form-Audio Generation Extension with Dual Lightweight Adapters},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y2CALC6B}},
  note         = {Machine review of arXiv:2508.11074}
}

abstract

Generating high-quality and temporally synchronized audio from video content is essential for video editing and post-production tasks, enabling the creation of semantically aligned audio for silent videos. However, most existing approaches focus on short-form audio generation for video segments under 10 seconds or rely on noisy datasets for long-form video-to-audio zsynthesis. To address these limitations, we introduce LD-LAudio-V1, an extension of state-of-the-art video-to-audio models and it incorporates dual lightweight adapters to enable long-form audio generation. In addition, we release a clean and human-annotated video-to-audio dataset that contains pure sound effects without noise or artifacts. Our method significantly reduces splicing artifacts and temporal inconsistencies while maintaining computational efficiency. Compared to direct fine-tuning with short training videos, LD-LAudio-V1 achieves significant improvements across multiple metrics: $FD_{\text{passt}}$ 450.00 $\rightarrow$ 327.29 (+27.27%), $FD_{\text{panns}}$ 34.88 $\rightarrow$ 22.68 (+34.98%), $FD_{\text{vgg}}$ 3.75 $\rightarrow$ 1.28 (+65.87%), $KL_{\text{panns}}$ 2.49 $\rightarrow$ 2.07 (+16.87%), $KL_{\text{passt}}$ 1.78 $\rightarrow$ 1.53 (+14.04%), $IS_{\text{panns}}$ 4.17 $\rightarrow$ 4.30 (+3.12%), $IB_{\text{score}}$ 0.25 $\rightarrow$ 0.28 (+12.00%), $Energy\Delta10\text{ms}$ 0.3013 $\rightarrow$ 0.1349 (+55.23%), $Energy\Delta10\text{ms(vs.GT)}$ 0.0531 $\rightarrow$ 0.0288 (+45.76%), and $Sem.\,Rel.$ 2.73 $\rightarrow$ 3.28 (+20.15%). Our dataset aims to facilitate further research in long-form video-to-audio generation and is available at https://github.com/deepreasonings/long-form-video2audio.

Discussion (0). Continue with ORCID to comment.

Reference graph

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" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...
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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

[2] [2]

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Honglie Chen, Weidi Xie, Andrea Vedaldi, and Andrew Zisserman. Vggsound: A large-scale audio-visual dataset. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 721--725. IEEE, 2020

work page 2020

[3] [3]

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Ziyang Chen, Prem Seetharaman, Bryan Russell, Oriol Nieto, David Bourgin, Andrew Owens, and Justin Salamon. Video-guided foley sound generation with multimodal controls. arXiv preprint arXiv:2411.17698, 2024

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[4] [4]

Taming multimodal joint training for high-quality video-to-audio synthesis

Ho Kei Cheng, Masato Ishii, Akio Hayakawa, Takashi Shibuya, Alexander Schwing, and Yuki Mitsufuji. Taming multimodal joint training for high-quality video-to-audio synthesis. arXiv preprint arXiv:2412.15322, 2024 a

arXiv 2024

[5] [5]

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Xin Cheng, Xihua Wang, Yihan Wu, Yuyue Wang, and Ruihua Song. Lova: Long-form video-to-audio generation. arXiv preprint arXiv:2409.15157, 2024 b . Long-form Video-to-Audio Generation

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[6] [6]

Rflav: Rolling flow matching for infinite audio video generation

Alex Ergasti, Giuseppe Gabriele Tarollo, Filippo Botti, Tomaso Fontanini, Claudio Ferrari, Massimo Bertozzi, and Andrea Prati. Rflav: Rolling flow matching for infinite audio video generation. arXiv preprint arXiv:2503.08307, 2025

arXiv 2025

[7] [7]

Skyreels‑audio: Omni audio‑conditioned talking portraits in video diffusion transformers

Zhengcong Fei, Hao Jiang, Di Qiu, Baoxuan Gu, Youqiang Zhang, Jiahua Wang, Jialin Bai, Debang Li, Mingyuan Fan, Guibin Chen, and Yahui Zhou. Skyreels‑audio: Omni audio‑conditioned talking portraits in video diffusion transformers. arXiv preprint arXiv:2506.00830, 2025

arXiv 2025

[8] [8]

Dense-localizing audio-visual events in untrimmed videos: A large-scale benchmark and baseline

Tiantian Geng, Teng Wang, Jinming Duan, Runmin Cong, and Feng Zheng. Dense-localizing audio-visual events in untrimmed videos: A large-scale benchmark and baseline. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22942--22951, 2023

work page 2023