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arxiv: 2606.25980 · v1 · pith:X3MBSKYO · submitted 2026-06-24 · cs.SD

FoleySet: A Multi-Level Human-Annotated Foley Sound Dataset

Sunshiyu Wang , Alexander Lerch This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-06-25 19:54 UTCgrok-4.3pith:X3MBSKYOrecord.jsonopen to challenge →

classification cs.SD
keywords Foley soundaudio datasetsound effectstaxonomyclassificationretrievalgeneration
0
0 comments X

The pith

FoleySet supplies 10,000 human-annotated clips under a two-level taxonomy to support Foley classification, retrieval, and generation.

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

The paper identifies the scarcity of high-quality annotated data for Foley sounds, which are recreated effects for on-screen actions in audiovisual production. It releases FoleySet, a collection of 10,000 clips labeled with a two-level taxonomy and offered under a Creative Commons license. The resource targets data-driven work on classifying existing sounds, retrieving matches for video, and generating new ones. A reader would care because professional Foley recording is labor-intensive, so better training data could enable scalable alternatives.

Core claim

We present FoleySet, a publicly available Foley dataset of 10,000 audio clips annotated with a two-level Foley taxonomy. This dataset provides a standardized, Creative Commons-licensed resource for data-driven Foley classification, retrieval, and generation.

What carries the argument

The two-level Foley taxonomy applied to annotate the 10,000 audio clips of human actions and prop interactions.

If this is right

  • Models for Foley classification can be trained directly on the labeled clips.
  • Retrieval systems can match sounds to specific on-screen actions using the taxonomy.
  • Generative models can learn to produce new Foley audio from the annotated examples.
  • Research groups gain a common, licensed benchmark for comparing methods.

Where Pith is reading between the lines

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

  • The two-level structure could support hierarchical training where coarse labels regularize fine-grained predictions.
  • Integration with video datasets might enable joint audio-visual models for automatic Foley creation.
  • Widespread adoption could create shared evaluation protocols similar to those used in speech recognition.

Load-bearing premise

High-quality annotated Foley datasets for training remain scarce and the new dataset with its two-level taxonomy will serve as a useful standardized resource.

What would settle it

A controlled experiment in which classifiers or generators trained on FoleySet show no accuracy or quality gain over models trained only on prior smaller collections would indicate the dataset does not deliver the expected benefit.

Figures

Figures reproduced from arXiv: 2606.25980 by Alexander Lerch, Sunshiyu Wang.

Figure 1
Figure 1. Figure 1: Overview of the FoleySet construction pipeline. mechanical, and music— along with a set of fine-grained and unambiguous leaf categories. This taxonomy was informed in part by sound sources frequently appearing in NYC 311 noise-complaint records. From this broader taxonomy, 10 classes were selected for UrbanSound8K: air conditioner, car horn, children playing, dog bark, drilling, engine idling, gun shot, ja… view at source ↗
Figure 2
Figure 2. Figure 2: Sub-category distribution within each major-category, sorted by clip count. The number of total clips per category is indicated in parentheses. review the audio quality of uploaded content and its user-provided tags and descriptions may be subjective or inconsistent, Stage 2 involved an initial manual screening to remove corrupted files, silent or excessively noisy clips, and recordings unrelated to Fo￾ley… view at source ↗
Figure 3
Figure 3. Figure 3: Confusion matrix on the major-category test set of Fo￾leySet. Toilet, and WhiteboardWriting all achieve an F1 score of 1.00, while Kiss (F1 = 0.96), Knock (F1 = 0.96), and Bubble (F1 = 0.91) also perform well. In contrast, several sub-categories are particularly difficult to recognize, including zero-recall classes such as Drip (support = 5), Latch (5), MetalDrop (4), StrawSip (2), WindowOpenClose (6), and… view at source ↗
Figure 4
Figure 4. Figure 4: Confusion matrix on the sub-category test set of FoleySet. 7. CONCLUSION We presented FoleySet, a novel human-annotated Foley dataset built upon a consistent and data-informed taxonomy. The work has two main contributions. First, it addresses the need for a Foley￾related dataset in an important yet underexplored subdomain of the broader field of sound effects, providing a resource that facilitates future F… view at source ↗
read the original abstract

In audiovisual post-production, Foley refers to synchronous sound effects associated with human actions, such as footsteps, cloth rustle, and prop handling, that are recreated to match the on-screen movements and interactions of characters. These sounds are often recorded by professional Foley artists using physical props. This resource-intensive workflow has motivated data-driven research on Foley, including tasks such as classification, retrieval, and generation; however, high-quality annotated Foley datasets for training remain scarce. To address this gap, we present FoleySet, a publicly available Foley dataset of 10,000 audio clips annotated with a two-level Foley taxonomy. This dataset provides a standardized, Creative Commons-licensed resource for data-driven Foley classification, retrieval, and generation.

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.

Referee Report

2 major / 0 minor

Summary. The paper presents FoleySet, a publicly available Creative Commons-licensed dataset of 10,000 audio clips annotated with a two-level Foley taxonomy, intended to address the scarcity of high-quality annotated resources for data-driven Foley classification, retrieval, and generation tasks.

Significance. If the annotations prove reliable and the taxonomy well-specified, the dataset could provide a useful standardized resource for Foley-related machine learning tasks. The contribution is a direct data release rather than a methodological advance, so its impact hinges entirely on the documented quality and reproducibility of the annotations.

major comments (2)
  1. [Abstract] Abstract: the central claims that the dataset is 'high-quality' and 'standardized' are unsupported because the manuscript provides no description of the annotation process, taxonomy construction, number of annotators, collection protocol, clip selection criteria, or any reliability statistics such as inter-annotator agreement.
  2. No section: without details on how the two-level taxonomy was defined or validated, it is impossible to assess whether the annotations meet the quality threshold asserted for training classification/retrieval/generation models.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. The comments correctly identify that the current manuscript lacks the necessary documentation on the annotation process and taxonomy to substantiate claims of high quality and standardization. We will address these points through revisions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claims that the dataset is 'high-quality' and 'standardized' are unsupported because the manuscript provides no description of the annotation process, taxonomy construction, number of annotators, collection protocol, clip selection criteria, or any reliability statistics such as inter-annotator agreement.

    Authors: We agree that the manuscript as submitted does not include these details, which are required to support the stated claims. In the revised version we will add a dedicated Methods section describing the two-level taxonomy construction, annotation protocol, number of annotators and their qualifications, clip sourcing and selection criteria, and any computed reliability statistics including inter-annotator agreement. revision: yes

  2. Referee: [—] No section: without details on how the two-level taxonomy was defined or validated, it is impossible to assess whether the annotations meet the quality threshold asserted for training classification/retrieval/generation models.

    Authors: We accept this assessment. The revised manuscript will contain an explicit section on taxonomy definition, validation procedures, and supporting evidence so that readers can evaluate suitability for downstream tasks. revision: yes

Circularity Check

0 steps flagged

Dataset release contains no derivations or predictions

full rationale

The paper is a direct announcement of a new 10k-clip Foley audio dataset with a two-level taxonomy and CC license. No equations, fitted parameters, predictions, or derivation chains exist in the abstract or described content. The contribution is the resource itself; claims of standardization rest on the release rather than any self-referential reduction or self-citation load-bearing step. This is a standard non-circular dataset paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Dataset release paper; no mathematical content, free parameters, axioms, or invented entities.

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discussion (0)

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Reference graph

Works this paper leans on

41 extracted references · 5 canonical work pages · 1 internal anchor

  1. [1]

    Examples include footsteps, cloth movement, and object interactions such as doors opening or coins jingling

    INTRODUCTION Foley refers to the synchronized reproduction of everyday sound effects for audiovisual media. Examples include footsteps, cloth movement, and object interactions such as doors opening or coins jingling. Unlike purely synthetic sound effects, Foley is often cre- ated and recorded in studio settings. In this process, Foley artists use a variet...

    work page arXiv 2026

  2. [2]

    FoleySet: A Multi-Level Human-Annotated Foley Sound Dataset

    EXISTING DATASETS This section reviews existing audio datasets that are relevant to FoleySet in terms of content, collection process, or labeling design. Their main characteristics are summarized in Table 1. DAFx.1 arXiv:2606.25980v1 [cs.SD] 24 Jun 2026 Proceedings of the 29th International Conference on Digital Audio Effects (DAFx26), Cambridge, MA, USA,...

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  3. [3]

    Existing audio taxonomies therefore show overlaps and inconsistencies in how Foley and other sound categories are labeled

    TAXONOMY Foley is a commonly used term in audiovisual sound post-production; however, there is no universally accepted definition of its scope, and its boundary with the broader domain of sound effects re- mains blurred. Existing audio taxonomies therefore show overlaps and inconsistencies in how Foley and other sound categories are labeled. For example, ...

  4. [4]

    All category and subcategory labels were assigned by a single annotator following the predefined taxonomy and annotation criteria

    DATASET CONSTRUCTION FoleySet is constructed through a multi-step pipeline designed to ensure that each audio clip is precisely labeled according to our proposed taxonomy while maintaining high audio quality and a balanced category distribution. All category and subcategory labels were assigned by a single annotator following the predefined taxonomy and a...

    2025

  5. [5]

    length 5 s) totaling approximately 9.5 hours of audio

    DATASET STATISTICS The released dataset contains 10,000 audio clips (44.1 kHz, 16 bit mono, max. length 5 s) totaling approximately 9.5 hours of audio. Clip durations range from 0.3 s to 5 s, with a mean of 3.4 s and a median of 4.3 s. The 10,000 clips originate from 5,739 unique Freesound recordings (1.7 clips per source on average), with 4,128 sources c...

    1949

  6. [6]

    For both tasks, we use PaSST (Patchout Spectrogram Transformer)

    BENCHMARK RESULTS We provide benchmark results for two classification tasks: (i)Task 1: 9-class major-category classification and (ii)Task 2: 73-class sub-category classification. For both tasks, we use PaSST (Patchout Spectrogram Transformer)

  7. [7]

    as a pre-trained feature extractor, specifically thehear21passt package [32], which provides AudioSet-pretrained checkpoints fol- lowing the HEAR 2021 NeurIPS Challenge API. For the feature extraction, all audio is resampled to 32 kHz and zero-padded to a Table 4:Per-class results for 9-way major-category classification on the FoleySet test set (N=1000). ...

    2021

  8. [8]

    The work has two main contributions

    CONCLUSION We presented FoleySet, a novel human-annotated Foley dataset built upon a consistent and data-informed taxonomy. The work has two main contributions. First, it addresses the need for a Foley- related dataset in an important yet underexplored subdomain of the broader field of sound effects, providing a resource that facilitates future Foley rese...

  9. [9]

    A deep learning approach to sound classification for film audio post-production,

    G. G. Peeters and J. D. Reiss, “A deep learning approach to sound classification for film audio post-production,” inProc. DAFx.7 Proceedings of the 29th International Conference on Digital Audio Effects (DAFx26), Cambridge, MA, USA, 1–4 September 2026 148th AES Convention, no. 10322, 2020

    2026

  10. [10]

    Unsupervised taxon- omy of sound effects,

    D. Moffat, D. Ronan, and J. D. Reiss, “Unsupervised taxon- omy of sound effects,” inProc. 20th Intl. Conf. on Digital Audio Effects (DAFx), Edinburgh, UK, 2017

    2017

  11. [11]

    Representation learning for the automatic indexing of sound effects libraries,

    A. B. Ma and A. Lerch, “Representation learning for the automatic indexing of sound effects libraries,” inProc. 23rd Int. Society for Music Information Retrieval Conf. (ISMIR), Bengaluru, India, 2022, pp. 387–394

    2022

  12. [12]

    Soundify: Matching sound effects to video,

    D. C.-E. Lin, A. Germanidis, C. Valenzuela, Y . Shi, and N. Martelaro, “Soundify: Matching sound effects to video,” inProc. ACM Symp. User Interface Software and Technology (UIST), San Francisco, CA, Oct. 29–Nov. 1, 2023

    2023

  13. [13]

    Foley sound synthesis at the dcase 2023 challenge,

    K. Choi, J. Im, L. M. Heller, B. McFee, K. Imoto, Y . Okamoto, M. Lagrange, and S. Takamichi, “Foley sound synthesis at the dcase 2023 challenge,” inProc. Workshop Detection and Clas- sification of Acoustic Scenes and Events (DCASE), Tampere, Finland, Sept. 21–22, 2023

    2023

  14. [14]

    T-FOLEY: A controllable waveform-domain diffusion model for temporal-event-guided foley sound synthesis,

    Y . Chung, J. Lee, and J. Nam, “T-FOLEY: A controllable waveform-domain diffusion model for temporal-event-guided foley sound synthesis,” inProc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Seoul, Korea, 2024

    2024

  15. [15]

    MambaFoley: Foley sound generation using selec- tive state-space models,

    M. F. Colombo, F. Ronchini, L. Comanducci, and F. An- tonacci, “MambaFoley: Foley sound generation using selec- tive state-space models,” inProc. IEEE International Confer- ence on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India, 2025, pp. 1–5

    2025

  16. [16]

    Autofoley: Artificial synthesis of synchronized sound tracks for silent videos with deep learn- ing,

    S. Ghose and J. J. Prevost, “Autofoley: Artificial synthesis of synchronized sound tracks for silent videos with deep learn- ing,”IEEE Trans. on Multimedia, vol. 23, pp. 1895–1907, 2021

    1907

  17. [17]

    Diff-Foley: Synchro- nized video-to-audio synthesis with latent diffusion models,

    S. Luo, C. Yan, C. Hu, and H. Zhao, “Diff-Foley: Synchro- nized video-to-audio synthesis with latent diffusion models,” inAdvances in Neural Information Processing Systems 36 (NeurIPS), New Orleans, LA, Dec. 10–16, 2023

    2023

  18. [18]

    FSD50K: An open dataset of human-labeled sound events,

    E. Fonseca, X. Favory, J. Pons, F. Font, and X. Serra, “FSD50K: An open dataset of human-labeled sound events,” IEEE/ACM Trans. on Audio, Speech, and Language Process- ing, vol. 30, pp. 829–852, 2022

    2022

  19. [19]

    Freesound

    Freesound. Freesound. Accessed: Sep. 2025. Available: https://freesound.org

    2025

  20. [20]

    A dataset and taxon- omy for urban sound research,

    J. Salamon, C. Jacoby, and J. P. Bello, “A dataset and taxon- omy for urban sound research,” inProc. of the 22nd ACM Intl. Conf. on Multimedia (MM), Orlando, FL, 2014

    2014

  21. [21]

    BBC Sound Effects

    BBC. BBC Sound Effects. Accessed: Sep.–Nov. 2025. Avail- able: https://sound-effects.bbcrewind.co.uk/

    2025

  22. [22]

    Foleybench: A benchmark for video-to-audio models,

    S. Dixit, K. Saito, Z. Zhong, Y . Mitsufuji, and C. Donahue, “Foleybench: A benchmark for video-to-audio models,” in Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Pro- cessing (ICASSP), Barcelona, Spain, May 4–8 2026

    2026

  23. [23]

    Mint: a multi-modal image and narrative text dub- bing dataset for foley audio content planning and generation,

    R. Fu, S. Shi, H. Guo, T. Wang, C. Qiang, Z. Wen, J. Tao, X. Qi, Y . Lu, X. Wang, Z. Wang, Y . Liu, X. Liu, S. Zhang, and G. Li, “Mint: a multi-modal image and narrative text dub- bing dataset for foley audio content planning and generation,” arXiv preprint arXiv:2406.10591, 2024

    work page arXiv 2024

  24. [24]

    6ksfx synth dataset,

    N. Garcia and J. Reiss, “6ksfx synth dataset,”arXiv preprint arXiv:2501.17198, 2025

    work page arXiv 2025

  25. [25]

    Au- dio set: An ontology and human-labeled dataset for audio events,

    J. F. Gemmeke, D. P. W. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, “Au- dio set: An ontology and human-labeled dataset for audio events,” inProc. IEEE Intl. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 2017

    2017

  26. [26]

    ESC: Dataset for Environmental Sound Classi- fication,

    K. J. Piczak, “ESC: Dataset for Environmental Sound Classification,” inProc. of the 23rd ACM Conf. on Multimedia (MM), 2015. [Online]. Available: http://dl.acm. org/citation.cfm?doid=2733373.2806390

    work page arXiv 2015

  27. [27]

    Universal Category System (UCS) category list,

    T. Nielsen, J. Drury, K. Paquinet al., “Universal Category System (UCS) category list,” https://universalcategorysystem. com/, 2024, version 8.2.1, accessed March 10, 2026

    2024

  28. [28]

    Ventriloquial acts: Critical reflections on the art of foley,

    M. Lewis, “Ventriloquial acts: Critical reflections on the art of foley,”The New Soundtrack, vol. 5, no. 2, pp. 103–120, 2015

    2015

  29. [29]

    Foley sounds vs. real sounds,

    S. Trento and A. de Götzen, “Foley sounds vs. real sounds,” in Proc. Sound and Music Computing Conference (SMC), 2011

    2011

  30. [30]

    The work of an invisible body: The contri- bution of foley artists to on-screen effort,

    L. F. Donaldson, “The work of an invisible body: The contri- bution of foley artists to on-screen effort,”Alphaville: Journal of Film and Screen Media, no. 7, 2014

    2014

  31. [31]

    Hollywood foley fx,

    Sound Ideas, “Hollywood foley fx,” Sound Ideas product page, 2025, accessed: Sep. 2025. [Online]. Available: https://sound-ideas.com/products/hollywood-foley-fx

    2025

  32. [32]

    Foley sound effects,

    ——, “Foley sound effects,” Sound Ideas product page, 2025, accessed: Sep. 2025. [Online]. Available: https: //sound-ideas.com/products/foley-sound-effects

    2025

  33. [33]

    Art of foley sound effects library,

    ——, “Art of foley sound effects library,” Sound Ideas product page, 2025, accessed: Sep. 2025. [Online]. Available: https://sound-ideas.com/products/ art-of-foley-sound-effects-library

    2025

  34. [34]

    Hd – foley sound effects,

    ——, “Hd – foley sound effects,” Sound Ideas product page, 2025, accessed: Sep. 2025. [Online]. Available: https://sound-ideas.com/products/hd-foley-sound-effects

    2025

  35. [35]

    Ultimate foley sound effects collection,

    ——, “Ultimate foley sound effects collection,” Sound Ideas product page, 2025, accessed: Sep. 2025. [Online]. Available: https://sound-ideas.com/products/ ultimate-foley-sound-effects-collection

    2025

  36. [36]

    Fight foley sound effects,

    ——, “Fight foley sound effects,” Sound Ideas product page, 2025, accessed: Sep. 2025. [Online]. Available: https://sound-ideas.com/products/fight-foley-sound-effects

    2025

  37. [37]

    Foley footsteps sound effects library,

    ——, “Foley footsteps sound effects library,” Sound Ideas product page, 2025, accessed: Sep. 2025. [Online]. Available: https://sound-ideas.com/products/ foley-footsteps-sound-effects-library

    2025

  38. [38]

    EBU R 128: Loudness nor- malisation and permitted maximum level of audio signals,

    European Broadcasting Union, “EBU R 128: Loudness nor- malisation and permitted maximum level of audio signals,” European Broadcasting Union (EBU), Geneva, Switzerland, Tech. Rep. R 128, 2011

    2011

  39. [39]

    Efficient training of audio transformers with patchout,

    K. Koutini, J. Schlüter, H. Eghbal-zadeh, and G. Widmer, “Efficient training of audio transformers with patchout,” in Proc. Interspeech, 2022, pp. 2753–2757

    2022

  40. [40]

    hear21passt: PaSST pre-trained models,

    K. Koutini, “hear21passt: PaSST pre-trained models,” https: //github.com/kkoutini/PaSST, 2022, accessed: Feb. 2, 2026. DAFx.8 Proceedings of the 29th International Conference on Digital Audio Effects (DAFx26), Cambridge, MA, USA, 1–4 September 2026

    2022

  41. [41]

    APPENDIX Table 6:Keyword-to-category mapping for the Foley taxonomy. Keywords Sub-category Major category Keywords Sub-category Major category walk; move; gravel; floor Walk Footstep tape; peel TapePeel Material-Interaction footstep; feet; foot; shoe; boot SingleStep Footstep writing; board WhiteboardWriting Material-Interaction run Run Footstep window; c...

    2026