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REVIEW 3 major objections 6 minor 54 references

Dialogue-Aware Video-to-Music Generation Using Public Domain Film Collections

T0 review · 3 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read The paper claims that conditioning video-to-music models on a per-frame dialogue track improves paired fidelity of generated film music, and introduces OSSL-v2, a 246.4-hour public-domain film corpus, as a reproducible benchmark.

desk verdict OSSL-v2 is a solid, durable dataset contribution; the dialogue-adapter claim is plausible but the current experiments do not separate dialogue from the architectural time-axis fix. read the letter →

arxiv 2608.11576 v1 pith:U66VYA2K submitted 2026-08-12 cs.SD cs.CVcs.MM

Haven Kim , Zachary Novack , Julian McAuley , Hao-Wen Dong This is my paper · ORCID
classification cs.SDcs.CVcs.MM
keywords OSSL-v2public-domainfilmdatasetvideo-to-musicgenerationdialogueconditioningFiLMmodulationtemporalfidelitysourceseparationreproduciblebenchmark
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 claims that spoken dialogue carries time-local cues that video-to-music models can exploit, and that feeding a separated dialogue track into the video-conditioning pathway, frame by frame, makes generated film music track the ground truth more closely. It also introduces OSSL-v2, a self-hosted dataset of 34,343 public-domain film clips totaling 246.4 hours, built so that researchers can train and evaluate on identical data without link rot. On both public-domain and commercial-film test sets, the dialogue adapter improves paired-fidelity metrics for two of the three backbones and on the out-of-distribution set for all three, while leaving distribution-level fidelity roughly unchanged. If the claim holds, dialogue is a useful conditioning signal for film scoring, and the field gains a durable benchmark.

What carries the argument

The central object is the dialogue adapter: a lightweight module that computes per-frame Feature-wise Linear Modulation (FiLM) parameters from the dialogue track's acoustic envelope, then applies $\tilde{v}_i = (1+\gamma_i)\odot v_i + \beta_i$ to each video-conditioning vector. Crucially, for the GVMGen backbone the adapter also restores a time axis to cross-attention memory by adding learned per-frame positional embeddings and sinusoidal key/value positional embeddings, so that dialogue can modulate individual frames instead of the whole conditioning sequence.

What would settle it

Retrain the adapter on the same data but replace the dialogue feature with its shuffled or phase-randomized envelope; if the CLAP-similarity and KL gains on OES-Com persist, the effect is driven by low-level energy timing, not dialogue content. Alternatively, evaluate on a low-residual-music set of hundreds of clips rather than 20, or use manually transcribed dialogue timing, to see whether the paired-fidelity gains survive.

Watch

Extended reading notes

Core claim

Starting from the observation that per-frame dialogue loudness and music loudness are negatively correlated in film (Pearson r = -0.11), the paper argues that dialogue is not just a global semantic cue but a locally timed one. The proposed adapter encodes the separated dialogue stem with a small 1-D convolutional layer, maps each frame's representation through a two-layer MLP to FiLM parameters, and modulates the video-conditioning vectors before cross-attention. For backbones whose video memory lacks a time axis, notably GVMGen, the adapter also adds learned per-frame positional embeddings and sinusoidal key/value positional embeddings so the dialogue modulation acts per frame rather than collapsing to a global shift. The paper claims this restores temporal identity to cross-attention and yields consistent paired-fidelity gains, with the largest improvements on the out-of-distribution commercial-film set.

Load-bearing premise

The load-bearing premise is that the isolated speech track used for conditioning truly represents the dialogue; the same source-separation model that built the dataset also produces this track, and the check that leftover music is not responsible is run on only 20 out-of-distribution clips, so the causal attribution to dialogue is the fragile link.

Editorial extensions

If this is right

  • OSSL-v2 gives the field a fixed, self-hosted benchmark: models from different groups can be trained and evaluated on exactly the same 34,343 clips, removing link rot and re-crawling as confounds.
  • Dialogue conditioning improves paired fidelity most on out-of-distribution commercial films, so a dialogue-aware model trained on public-domain films should transfer better to real film scoring inputs.
  • Because the gains persist on low-residual-music subsets, the paper attributes the improvements to dialogue content rather than to music leaking into the separated speech track.
  • Dialogue conditioning does not systematically change the generated music's overall distribution, so it acts as a local timing and regularization signal rather than a global style control.
  • For backbones without a time axis in their video memory, restoring positional identity to cross-attention is a necessary part of the adapter; without it, per-frame FiLM collapses into a global shift.

Reading between the lines

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

  • The adapter's mechanism is not dialogue-specific: the same per-frame FiLM conditioning could carry other time-local signals such as sound-effect onsets, scene cuts, or action beats, and might improve alignment for those modalities as well.
  • A stronger causal test would use manually transcribed or script-derived dialogue timing, or a synthetic dialogue overlay on music-free video, to bypass separation uncertainty entirely; the current 20-clip OES-Com low-residual-music control is too small to fully close that gap.
  • The observed negative correlation between dialogue and music loudness suggests the model may learn a ducking-like behavior; a listener study or LUFS analysis around speech onsets could confirm whether the adapter produces audible speech-music separation.
  • OSSL-v2's public-domain provenance means the corpus can be legally extended and redistributed, so follow-up work can scale it or add dialogue annotations without copyright negotiation.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper introduces OSSL-v2, a self-hosted dataset of 34,343 video-music clips (246.4 hours) derived from public-domain films, and studies dialogue as a frame-level conditioning signal for video-to-music generation. The authors augment three existing open-source models (VidMuse, GVMGen, Diff-V2M) with a dialogue adapter that extracts a per-frame dialogue envelope via source separation, encodes it with a small convnet, and applies FiLM modulation to video-conditioning features, with additional time-axis restoration for GVMGen. They evaluate on the OSSL-v2 test set and on OES-Com, a commercial-film set, using FAD, Precision, Recall, CLAP similarity, and audio-event KL divergence. The paper reports that the dialogue adapter improves paired fidelity in most conditions, especially on the out-of-distribution set, while distributional metrics are largely unchanged. It also runs a low-residual-music (LRM) subset analysis to argue that gains are not due to residual music in the separated dialogue stem.

Significance. The dataset contribution is significant and timely: OSSL-v2 is a self-hosted, reproducible, copyright-conscious corpus that is substantially larger than the prior OSSL and avoids the link-rot problems of URL-crawled corpora, making it a credible benchmark for video-to-music generation. The dialogue-conditioning idea is also interesting and, if validated, would point to a useful and underused conditioning signal for film-music generation. However, the paper's central claim that dialogue itself drives the observed improvements is not yet established, because the GVMGen comparison conflates the dialogue adapter with an architectural time-axis restoration, and because the reported differences lack error bars and significance tests. The paper is well structured and the experimental design is otherwise careful, including a 9:1 split, fixed hyperparameters, and an LRM control that addresses one plausible confound. The fixable nature of these issues makes the work suitable for major revision rather than rejection.

major comments (3)
  1. [Section 4, GVMGen paragraph] The comparison between GVMGen and GVMGen(+Dialogue) is confounded. The text states that applying Equation 1 directly to GVMGen's mean-pooled Q-Former outputs would collapse to a global effect, so the authors add two zero-initialized components: learned per-frame positional embeddings on the Q-Former outputs and fixed sinusoidal positional embeddings on the cross-attention keys/values. These are architectural changes that restore a time axis independently of the dialogue-derived FiLM parameters. As a result, the improvement (or regression) observed for GVMGen(+Dialogue) could come from the time-axis restoration alone, from the dialogue FiLM, or from their interaction. A time-axis-only control—the same two positional-embedding additions without the dialogue-modulated FiLM—is needed to attribute the effect to dialogue. The LRM control does not resolve this, because those two additions are present in both the full and LRM +Dialogue variants.
  2. [Table 2 and Section 6, 'Effect of the dialogue adapter'] No error bars, confidence intervals, or significance tests are reported for any of the metrics in Table 2. Several headline differences are small (e.g., OSSL-v2 CLAP for GVMGen 0.43→0.39, KL 0.72→0.73; OSSSL-v2 CLAP for Diff-V2M 0.26→0.27) and could plausibly be within run-to-run noise, especially for autoregressive and diffusion models. The in-distribution GVMGen regression is particularly concerning for the claim of consistent improvement. Since the protocol is fixed and reproducible, the authors should provide multiple training seeds (or at least bootstrap confidence intervals on the evaluation metrics) and a statistical test to support the claim that the adapter changes paired fidelity.
  3. [Section 5, low-residual-music (LRM) paragraph] The LRM analysis is described as assessing whether 'any benefit from dialogue conditioning is merely an artifact of residual music leaking into the separated dialogue stem,' but it does not establish that the benefit comes from dialogue as opposed to the time-axis changes. The LRM filter only removes clips where the separated dialogue stem contains detectable music; it does not remove the positional-embedding confound for GVMGen. Additionally, the OES-Com LRM subset contains only 20 clips, and with five metrics and three backbones, conclusions drawn from this subset are fragile. The authors should either enlarge the LRM subset (e.g., by collecting more commercial clips) or treat the LRM results as qualitative rather than evidential.
minor comments (6)
  1. [Section 4, first paragraph] There are typos: 'incoporate' should be 'incorporate' and 'pen-source' should be 'open-source'.
  2. [Section 4, VidMuse paragraph] The phrase 'already temporararily order' should read 'already temporally ordered'.
  3. [Section 4, Diff-V2M paragraph] 'whith' should be 'with' in the sentence 'whith the FiLM module initialized as an identity function.'
  4. [References] References [17] and [20] both list arXiv:1704.06761; the entry for Diff-BGM [20] appears to have the wrong arXiv identifier and should be corrected.
  5. [Section 5, evaluation metrics paragraph] The sentence 'Finally, we measurediversity using Recall [45]' is missing a space and subject-verb agreement; it should be 'Finally, we measure diversity using Recall [45].'
  6. [Figure 1 caption] The caption reads 'Binned ialogue vs. music loudness' and should read 'Binned dialogue vs. music loudness.'

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the dialogue adapter and dataset are evaluated on held-out fixed baselines, with no fitted parameter or self-citation that forces the reported outcomes.

full rationale

The paper's central claims—that OSSL-v2 is a reproducible video–music benchmark and that a dialogue-conditioned FiLM adapter improves paired fidelity over three open backbones—are supported by held-out evaluations on an OSSL-v2 test split and on the fixed OES-Com set, compared against the unmodified baseline models. No equation in Section 4 reduces to a fitted quantity: the adapter is zero-initialized ('The final layer of φ is zero-initialized, so that the module is initialized as an identity function at the start of training') and its parameters are learned from training data, not derived from the evaluation metrics. The dataset preprocessing thresholds (e.g., the non-music probability threshold 'empirically set to 0.05') are construction choices and do not by construction determine FAD, KL, CLAP, Precision, or Recall on the test set. The use of the same source-separation model [37] both to construct OSSL-v2 and to isolate dialogue is a potential confound, and the OES-Com benchmark originates in the authors' prior OSSL work [21], so the evaluation set is partly self-cited; however, the LRM control is designed to address leakage, OES-Com is a fixed external collection of commercial film clips, and the reported metrics are not implied by the construction. A separate attribution concern is that the GVMGen comparison confounds dialogue with the two added time-axis components (learned per-frame positional embeddings and fixed sinusoidal positional embeddings on cross-attention keys and values) described in Section 4, so some gains may stem from restoring temporal identity rather than from dialogue itself; this is an experimental attribution issue, not a case where the 'prediction' equals its input by definition. No circular step can be exhibited with a specific equation or fitted-parameter reduction, so the appropriate finding is no significant circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper relies on prior source-separation and event-detection models, a manual category selection, and handset thresholds. These are assumptions from prior art or dataset-construction choices, not new entities. The central comparison is self-contained on held-out data.

free parameters (3)
  • non_music_probability_threshold = 0.05
    Empirically chosen threshold to filter clips where source-separated music still contains non-musical events. Affects dataset composition but not the dialogue-adapter comparison directly.
  • low_residual_music_threshold = 0.05 summed music-event probability
    Threshold on PANNs music-event probabilities in the separated dialogue stem to define LRM subsets. The OES-Com LRM subset is only 20 clips, making conclusions fragile.
  • training_segment_length = 10 seconds
    Set because the shortest OSSL-v2 clips are 10 seconds, while original models used 30 seconds. This changes the training distribution for all compared models equally.
assumptions (4)
  • domain assumption Source separation model [37] accurately separates dialogue from music in film audio.
    Both dataset construction and the dialogue conditioning rely on this separation being clean. If leakage is high, the dialogue adapter may actually be responding to music.
  • domain assumption The acoustic envelope (loudness/energy) of dialogue carries enough information about musical timing to improve generation.
    Motivated by a weak correlation (r=-0.11) and general film-scoring practice. The model does not use semantic content of speech.
  • domain assumption CLAP similarity and PaSST-based KL divergence of audio-event distributions are valid proxies for paired fidelity.
    Used without validation against human perception; no listening tests.
  • domain assumption The 157 PANNs categories manually selected as musical correctly identify music events.
    Manual selection; threshold 0.05 for non-music probability is empirical.

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Cite this review

Pith. "Pith review of Dialogue-Aware Video-to-Music Generation Using Public Domain Film Collections." pith.science (2026). https://pith.science/paper/U66VYA2K

@misc{pith2026260811576,
  author       = {Pith},
  title        = {Pith review of: Dialogue-Aware Video-to-Music Generation Using Public Domain Film Collections},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/U66VYA2K}},
  note         = {Machine review of arXiv:2608.11576}
}
read the original abstract

Video-to-music generation has drawn growing interest for its role in conveying the emotion of visual media, including film. Progress in the field, however, is hampered by a reproducibility gap: models are often trained on crawled corpora referenced through YouTube URLs that may be deleted, with the underlying data often difficult and time-consuming to retrieve. To address this, we introduce the Open Screen Soundtrack Library version 2 (OSSL-v2), a self-hosted corpus of 34,343 video clips totaling 246.4 hours, sourced from public-domain films. Unlike crawled corpora, OSSL-v2 is reproducible (i.e., not subject to link rot) and copyright-conscious, yet still large enough to train functional video-to-music models. We then use this film-domain corpus to study dialogue as a conditioning signal for video-to-music generation, motivated by the close temporal coupling between film music and on-screen speech. Specifically, we augment existing models' video cross-attention with a time axis and modulate it frame-by-frame with the dialogue track. Evaluated on both public-domain and commercial films, our approach shows improvement over the state-of-the-art baselines. The dataset is available at https://huggingface.co/datasets/McAuley-Lab/OSSL-v2.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

54 extracted references · 36 canonical work pages

  1. [1]

    INTRODUCTION Music is central to how video communicates: it conveys emo- tion, establishes mood, and shapes a viewer’s interpretation of a scene [1, 2]. Video-to-music generation systems, which automatically generate music that matches the emotional con- tent of a video [3, 4, 5, 6], have therefore attracted growing interest and remain an important direct...

    work page Pith review arXiv 2026

  2. [2]

    Early work in modern video-to-music generation systems uses large-scale web music-video corpora and autoregressive modeling over semantic acoustic tokens to generate music [8]

    RELA TED WORK In this section, we review prior work on video-to-music gen- eration and their datasets, with an emphasis on musicaudio generation rather than symbolic music. Early work in modern video-to-music generation systems uses large-scale web music-video corpora and autoregressive modeling over semantic acoustic tokens to generate music [8]. Subsequ...

    work page

  3. [3]

    trance music

    OPEN SCREEN SOUNDTRACK LIBRARY VERSION 2 Dataset Construction.Our video-music dataset, Open Screen Soundtrack Library Version 2 (OSSL-v2), is con- structed from 1,886 public domain films downloaded from YouTube. The dataset is self-hosted, meaning that readers do not need to undergo a separate download process such as web scraping. Our dataset constructio...

    work page

  4. [4]

    DIALOGUE-A W ARE VIDEO-TO-MUSIC GENERA TION In film, dialogue provides important cues about character in- teraction, narrative emphasis, and emotional intensity [22]. These cues occur at specific moments in time, so a video-to- music model should be able to condition the generated score on when dialogue appears and how it aligns with the visual content, r...

    work page

  5. [5]

    First, we bench- mark video-to-music generation tasks by comparing existing video-to-music generation models trained and evaluated on identical data, using OSSL-v2

    EXPERIMENTS The goal of our experiments are two-fold. First, we bench- mark video-to-music generation tasks by comparing existing video-to-music generation models trained and evaluated on identical data, using OSSL-v2. Second, we assess the ef- fectivenss of the proposed dialogue adapter when applied to these models. Compared Models.We comprehensively ben...

    work page

  6. [6]

    +Dialogue

    RESULTS Table 2 reports the objective evaluation results for all com- pared models on both the public-domain test set and the com- mercial OES-Com set. Benchmarking video-to-music models.On the public- domain OSSL-v2 test set, GVMGen achieves the strongest overall performance, followed by VidMuse and Diff-V2M. Diff-V2M is a notable outlier in distribution...

    work page

  7. [7]

    Be- cause the dataset is free from link rot and does not require separate web scraping, our dataset is suitable as a durable benchmark for the field

    CONCLUSION We introduced OSSL-v2, a large-scale, self-hosted corpus of music-film clip pairs sourced from public-domain films. Be- cause the dataset is free from link rot and does not require separate web scraping, our dataset is suitable as a durable benchmark for the field. Beyond the dataset, we showed that dialogue is an informative conditioning signa...

    work page

  8. [8]

    Teaser Generation for Long Documentaries and Educational Videos

    ACKNOWLEDGEMENT This work is partially supported by the NVIDIA Academic Grant Program under the project titled “Teaser Generation for Long Documentaries and Educational Videos”

    work page

Show all 54 references
  1. [9]

    Attendaffectnet–emotion pre- diction of movie viewers using multimodal fusion with self-attention,

    Ha Thi Phuong Thao, BT Balamurali, Gemma Roig, and Dorien Herremans, “Attendaffectnet–emotion pre- diction of movie viewers using multimodal fusion with self-attention,”Sensors, vol. 21, no. 24, pp. 8356, 2021

    2021

  2. [10]

    Predicting emotion from music videos: exploring the relative contribution of vi- sual and auditory information to affective responses,

    Phoebe Chua, Dimos Makris, Dorien Herremans, Gemma Roig, and Kat Agres, “Predicting emotion from music videos: exploring the relative contribution of vi- sual and auditory information to affective responses,” arXiv preprint arXiv:2202.10453, 2022

    2022 arXiv

  3. [11]

    The cognitive processing of film and musical soundtracks,

    Marilyn G Boltz, “The cognitive processing of film and musical soundtracks,”Memory & Cognition, vol. 32, no. 7, pp. 1194–1205, 2004

    2004

  4. [12]

    Multimodal deep models for predicting affec- tive responses evoked by movies.,

    Ha Thi Phuong Thao, Dorien Herremans, and Gemma Roig, “Multimodal deep models for predicting affec- tive responses evoked by movies.,” inICCV Workshops, 2019, pp. 1618–1627

    2019

  5. [13]

    On music’s potential to convey meaning in film: A systematic review of empirical evi- dence,

    Ann-Kristin Herget, “On music’s potential to convey meaning in film: A systematic review of empirical evi- dence,”Psychology of Music, vol. 49, no. 1, pp. 21–49, 2021

    2021

  6. [14]

    Emotion embedding spaces for matching music to stories,

    Minz Won, Justin Salamon, Nicholas J Bryan, Gau- tham J Mysore, and Xavier Serra, “Emotion embedding spaces for matching music to stories,”arXiv preprint arXiv:2111.13468, 2021

    2021 arXiv

  7. [15]

    Foley music: Learning to generate music from videos,

    Chuang Gan, Deng Huang, Peihao Chen, Joshua B Tenenbaum, and Antonio Torralba, “Foley music: Learning to generate music from videos,” inComputer Vision–ECCV 2020: 16th European Conference, Glas- gow, UK, August 23–28, 2020, Proceedings, Part XI 16. Springer, 2020, pp. 758–775

    2020

  8. [16]

    V2meow: Meow- ing to the visual beat via video-to-music generation,

    Kun Su, Judith Yue Li, Qingqing Huang, Dima Kuzmin, Joonseok Lee, Chris Donahue, Fei Sha, Aren Jansen, Yu Wang, Mauro Verzetti, et al., “V2meow: Meow- ing to the visual beat via video-to-music generation,” in Proceedings of the AAAI Conference on Artificial Intel- ligence, 202...

    2024

  9. [17]

    Vidmuse: A simple video-to-music generation framework with long-short-term modeling,

    Zeyue Tian, Zhaoyang Liu, Ruibin Yuan, Jiahao Pan, Qifeng Liu, Xu Tan, Qifeng Chen, Wei Xue, and Yike Guo, “Vidmuse: A simple video-to-music generation framework with long-short-term modeling,” 2025

    2025

  10. [18]

    Vmas: Video-to-music generation via semantic alignment in web music videos,

    Yan-Bo Lin, Yu Tian, Linjie Yang, Gedas Bertasius, and Heng Wang, “Vmas: Video-to-music generation via semantic alignment in web music videos,” in2025 IEEE/CVF Winter Conference on Applications of Com- puter Vision (WACV). IEEE, 2025, pp. 1155–1165

    2025

  11. [19]

    Sonique: Video background music generation using unpaired audio- visual data,

    Liqian Zhang and Magdalena Fuentes, “Sonique: Video background music generation using unpaired audio- visual data,” inICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025, pp. 1–5

    2025

  12. [20]

    Gvmgen: A general video-to-music genera- tion model with hierarchical attentions,

    Heda Zuo, Weitao You, Junxian Wu, Shihong Ren, Pei Chen, Mingxu Zhou, Yujia Lu, and Lingyun Sun, “Gvmgen: A general video-to-music genera- tion model with hierarchical attentions,”arXiv preprint arXiv:2501.09972, 2025

    2025 arXiv

  13. [21]

    Vision-to-music generation: A survey,

    Zhaokai Wang, Chenxi Bao, Le Zhuo, Jingrui Han, Yang Yue, Yihong Tang, Victor Shea-Jay Huang, and Yue Liao, “Vision-to-music generation: A survey,” arXiv preprint arXiv:2503.21254, 2025

    2025 arXiv

  14. [22]

    Ai- based chinese-style music generation from video con- tent: a study on cross-modal analysis and generation methods,

    Moxi Cao, Jiaxiang Zheng, and Chongbin Zhang, “Ai- based chinese-style music generation from video con- tent: a study on cross-modal analysis and generation methods,”EURASIP Journal on Audio, Speech, and Music Processing, vol. 2025, no. 1, pp. 8, 2025

    2025

  15. [23]

    V2m- zero: Zero-pair time-aligned video-to-music genera- tion,

    Yan-Bo Lin, Jonah Casebeer, Long Mai, Aniruddha Ma- hapatra, Gedas Bertasius, and Nicholas J Bryan, “V2m- zero: Zero-pair time-aligned video-to-music genera- tion,”arXiv preprint arXiv:2603.11042, 2026

    2026 arXiv

  16. [24]

    Diff- v2m: A hierarchical conditional diffusion model with explicit rhythmic modeling for video-to-music genera- tion,

    Shulei Ji, Zihao Wang, Jiaxing Yu, Xiangyuan Yang, Shuyu Li, Songruoyao Wu, and Kejun Zhang, “Diff- v2m: A hierarchical conditional diffusion model with explicit rhythmic modeling for video-to-music genera- tion,” inProceedings of the AAAI Conference on Artifi- cial Intelligen...

    2026

  17. [26]

    Quan- tized gan for complex music generation from dance videos,

    Ye Zhu, Kyle Olszewski, Yu Wu, Panos Achlioptas, Menglei Chai, Yan Yan, and Sergey Tulyakov, “Quan- tized gan for complex music generation from dance videos,” 2022

    2022

  18. [27]

    Video background music genera- tion: Dataset, method and evaluation,

    Le Zhuo, Zhaokai Wang, Baisen Wang, Yue Liao, Chenxi Bao, Stanley Peng, Songhao Han, Aixi Zhang, Fei Fang, and Si Liu, “Video background music genera- tion: Dataset, method and evaluation,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 15...

    2023

  19. [28]

    Diff-bgm: A diffusion model for video back- ground music generation,

    Sizhe Li, Yiming Qin, Minghang Zheng, Xin Jin, and Yang Liu, “Diff-bgm: A diffusion model for video back- ground music generation,”arXiv:1704.06761, 2024

    2024 arXiv

  20. [29]

    Video-guided text- to-music generation using public domain movie collec- tions,

    Haven Kim, Zachary Novack, Weihan Xu, Julian McAuley, and Hao-Wen Dong, “Video-guided text- to-music generation using public domain movie collec- tions,”arXiv preprint arXiv:2506.12573, 2025

    2025 arXiv

  21. [30]

    Acoustic profiles in vocal emotion expression.,

    Rainer Banse and Klaus R Scherer, “Acoustic profiles in vocal emotion expression.,”Journal of personality and social psychology, vol. 70, no. 3, pp. 614, 1996

    1996

  22. [31]

    Background ducking to produce esthetically pleasing audio for tv with clear speech,

    Matteo Torcoli, Alex Freke-Morin, Jouni Paulus, Chris- tian Simon, and Ben Shirley, “Background ducking to produce esthetically pleasing audio for tv with clear speech,” inAudio Engineering Society Convention 146. Audio Engineering Society, 2019

    2019

  23. [32]

    Improving dialogue intelligi- bility in streaming media,

    Tim Addy, Scott Norcross, Mike Ward, Pau Arumi, and Heidi-Maria Lehtonen, “Improving dialogue intelligi- bility in streaming media,” inProceedings of the 5th Mile-High Video Conference, 2026, pp. 34–35

    2026

  24. [33]

    Creating a multitrack clas- sical music performance dataset for multimodal mu- sic analysis: Challenges, insights, and applications,

    Bochen Li, Xinzhao Liu, Karthik Dinesh, Zhiyao Duan, and Gaurav Sharma, “Creating a multitrack clas- sical music performance dataset for multimodal mu- sic analysis: Challenges, insights, and applications,” IEEE Transactions on Multimedia, vol. 21, no. 2, pp. 522–535, Feb. 2019

    2019

  25. [34]

    Video2music: Suitable music generation from videos using an affective multimodal transformer model,

    Jaeyong Kang, Soujanya Poria, and Dorien Herremans, “Video2music: Suitable music generation from videos using an affective multimodal transformer model,”Ex- pert Systems with Applications, vol. 249, pp. 123640, Sept. 2024

    2024

  26. [35]

    Extending visual dynamics for video-to-music genera- tion,

    Xiaohao Liu, Teng Tu, Yunshan Ma, and Tat-Seng Chua, “Extending visual dynamics for video-to-music genera- tion,”arXiv:2504.07594, 2025

    2025 arXiv

  27. [36]

    Vid- musician: Video-to-music generation with semantic- rhythmic alignment via hierarchical visual features,

    Sifei Li, Binxin Yang, Chunji Yin, Chong Sun, Yuxin Zhang, Weiming Dong, and Chen Li, “Vid- musician: Video-to-music generation with semantic- rhythmic alignment via hierarchical visual features,” arXiv:2412.06296, 2024

    2024 arXiv

  28. [37]

    Muvi: Video-to-music gen- eration with semantic alignment and rhythmic synchro- nization,

    Ruiqi Li, Siqi Zheng, Xize Cheng, Ziang Zhang, Sheng- peng Ji, and Zhou Zhao, “Muvi: Video-to-music gen- eration with semantic alignment and rhythmic synchro- nization,”arXiv:2410.12957, 2024

    2024 arXiv

  29. [38]

    Video echoed in music: Semantic, temporal, and rhythmic alignment for video-to-music generation,

    Xinyi Tong, Yiran Zhu, Jishang Chen, Chunru Zhan, Tianle Wang, Sirui Zhang, Nian Liu, Tiezheng Ge, Duo Xu, Xin Jin, Feng Yu, and Song-Chun Zhu, “Video echoed in music: Semantic, temporal, and rhythmic alignment for video-to-music generation,” arXiv:2511.09585, 2025

    2025

  30. [39]

    Video-robin: Autoregressive diffusion planning for intent-grounded video-to-music generation,

    Vaibhavi Lokegaonkar, Aryan Vijay Bhosale, Vishnu Raj, Gouthaman KV , Ramani Duraiswami, Lie Lu, Sreyan Ghosh, and Dinesh Manocha, “Video-robin: Autoregressive diffusion planning for intent-grounded video-to-music generation,”arXiv:2604.17656, 2026

    2026 arXiv

  31. [40]

    Harmonizing pixels and melodies: Maestro-guided film score generation and composition style transfer,

    F. Qi, L. Ni, and C. Xu, “Harmonizing pixels and melodies: Maestro-guided film score generation and composition style transfer,”arXiv:2411.07539, 2024

    2024 arXiv

  32. [41]

    Filmcomposer: Llm-driven music production for silent film clips,

    Zhifeng Xie, Qile He, Youjia Zhu, Qiwei He, and Meng- tian Li, “Filmcomposer: Llm-driven music production for silent film clips,”arXiv:2503.08147, 2025

    2025 arXiv

  33. [42]

    M 2ugen: Multi-modal mu- sic understanding and generation with the power of large language models,

    Shansong Liu, Atin Sakkeer Hussain, Qilong Wu, Chen- shuo Sun, and Ying Shan, “M 2ugen: Multi-modal mu- sic understanding and generation with the power of large language models,”arXiv:2311.11255, 2024

    2024 arXiv

  34. [43]

    Mumu-llama: Multi-modal music understanding and generation via large language models,

    Shansong Liu, Atin Sakkeer Hussain, Qilong Wu, Chen- shuo Sun, and Ying Shan, “Mumu-llama: Multi-modal music understanding and generation via large language models,”arXiv:2412.06660, 2024

    2024 arXiv

  35. [44]

    Multimodal music generation with explicit bridges and retrieval augmentation,

    Baisen Wang, Le Zhuo, Zhaokai Wang, Chenxi Bao, Wu Chengjing, Xuecheng Nie, Jiao Dai, Jizhong Han, Yue Liao, and Si Liu, “Multimodal music generation with explicit bridges and retrieval augmentation,”arXiv preprint arXiv:2412.09428, 2024

    2024 arXiv

  36. [45]

    Benchmarks and leaderboards for sound demixing tasks,

    Roman Solovyev, Alexander Stempkovskiy, and Tatiana Habruseva, “Benchmarks and leaderboards for sound demixing tasks,”arXiv:2305.07489, 2023

    2023 arXiv

  37. [46]

    Panns: Large- scale pretrained audio neural networks for audio pat- tern recognition,

    Qiuqiang Kong, Yin Cao, Turab Iqbal, Yuxuan Wang, Wenwu Wang, and Mark D Plumbley, “Panns: Large- scale pretrained audio neural networks for audio pat- tern recognition,”IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 2880– 2894, 2020

    2020

  38. [47]

    Film: Visual reasoning with a general conditioning layer,

    Ethan Perez, Florian Strub, Harm De Vries, Vincent Du- moulin, and Aaron Courville, “Film: Visual reasoning with a general conditioning layer,” inProceedings of the AAAI conference on artificial intelligence, 2018, vol. 32

    2018

  39. [48]

    Simple and controllable music generation,

    Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, and Alexandre D´efossez, “Simple and controllable music generation,” Advances in neural information processing systems, vol. 36, pp. 47704–47720, 2023

    2023

  40. [49]

    Learning transferable visual models from natural lan- guage supervision,

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sas- try, Amanda Askell, Pamela Mishkin, Jack Clark, et al., “Learning transferable visual models from natural lan- guage supervision,” inInternational conference on ma- chine lear...

    2021

  41. [50]

    Blip-2: Bootstrapping language-image pre- training with frozen image encoders and large language models,

    Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi, “Blip-2: Bootstrapping language-image pre- training with frozen image encoders and large language models,” inInternational conference on machine learn- ing. PMLR, 2023, pp. 19730–19742

    2023

  42. [51]

    Stable audio open,

    Zach Evans, Julian D Parker, CJ Carr, Zack Zukowski, Josiah Taylor, and Jordi Pons, “Stable audio open,” inICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025, pp. 1–5

    2025

  43. [52]

    Fr\’echet audio distance: A metric for evaluating music enhancement algorithms,

    Kevin Kilgour, Mauricio Zuluaga, Dominik Roblek, and Matthew Sharifi, “Fr\’echet audio distance: A metric for evaluating music enhancement algorithms,”arXiv preprint arXiv:1812.08466, 2018

    2018 arXiv

  44. [53]

    Reliable fidelity and diversity metrics for generative models,

    Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo, “Reliable fidelity and diversity metrics for generative models,” inInter- national conference on machine learning. PMLR, 2020, pp. 7176–7185

    2020

  45. [54]

    Large-scale contrastive language-audio pretraining with feature fu- sion and keyword-to-caption augmentation,

    Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Tay- lor Berg-Kirkpatrick, and Shlomo Dubnov, “Large-scale contrastive language-audio pretraining with feature fu- sion and keyword-to-caption augmentation,” inICASSP 2023-2023 IEEE International Conference on Acous- tics, Speech a...

    2023

  46. [55]

    Efficient training of audio transform- ers with patchout,

    Khaled Koutini, Jan Schl ¨uter, Hamid Eghbal-Zadeh, and Gerhard Widmer, “Efficient training of audio transform- ers with patchout,”arXiv preprint arXiv:2110.05069, 2021

    2021 arXiv

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.