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arxiv: 2510.24332 · v3 · submitted 2025-10-28 · 💻 cs.SD · cs.CV· eess.AS· eess.IV

Sound Source Localization for Spatial Mapping of Surgical Actions in Dynamic Scenes

Jonas Hein , Lazaros Vlachopoulos , Maurits Geert Laurent Olthof , Bastian Sigrist , Philipp F\"urnstahl , Matthias Seibold This is my paper

Pith reviewed 2026-05-18 03:28 UTC · model grok-4.3

classification 💻 cs.SD cs.CVeess.ASeess.IV
keywords sound source localizationsurgical scene understandingmultimodal fusionacoustic event detectiondynamic point cloudsphased microphone arrayRGB-D cameratransformer model
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The pith

A framework projects 3D sound localizations from a microphone array onto dynamic point clouds to map surgical tool-tissue interactions.

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

The paper develops a system that creates four-dimensional audio-visual models of surgical scenes by combining sound data with visual point clouds. A transformer module first identifies time segments with relevant acoustic events like tool-tissue contacts. A phased microphone array then localizes those sounds in three-dimensional space, and the locations are projected onto point clouds captured by an RGB-D camera. This produces a dynamic representation that links sounds to specific visual elements in the operating room. The approach was tested in simulated procedures to show accurate localization and multimodal fusion.

Core claim

The central claim is that projecting acoustic localization information from a phased microphone array onto dynamic point clouds from an RGB-D camera, after using a transformer to detect relevant tool-tissue interaction segments, produces the first spatially and temporally aware multimodal representations of dynamic surgical scenes.

What carries the argument

Projection of 3D acoustic localizations from a phased microphone array onto dynamic RGB-D point clouds, after transformer-based detection of acoustic events.

If this is right

  • Surgical acoustic events become associated with specific visual scene elements in 3D.
  • The resulting 4D representations provide richer contextual understanding than vision alone.
  • The method supplies a foundation for future intelligent and autonomous surgical systems.
  • Multimodal data fusion enables temporally and spatially aware modeling of surgical activity.

Where Pith is reading between the lines

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

  • Audio cues could help detect subtle tissue changes or errors not visible in standard video.
  • The same projection technique might apply to other noisy, dynamic environments such as assembly lines.
  • Combining this with existing surgical navigation tools could improve real-time feedback for surgeons.

Load-bearing premise

Acoustic events from tool-tissue interactions can be reliably detected by the transformer and accurately localized in 3D by the phased array without major interference from operating room noise or movement.

What would settle it

Real operating room recordings where localization errors exceed a few centimeters or where detected sounds fail to align with visible tool actions in the point clouds.

read the original abstract

Purpose: Surgical scene understanding is key to advancing computer-aided and intelligent surgical systems. Current approaches predominantly rely on visual data or end-to-end learning, which limits fine-grained contextual modeling. This work aims to enhance surgical scene representations by integrating 3D acoustic information, enabling temporally and spatially aware multimodal understanding of surgical environments. Methods: We propose a novel framework for generating 4D audio-visual representations of surgical scenes by projecting acoustic localization information from a phased microphone array onto dynamic point clouds from an RGB-D camera. A transformer-based acoustic event detection module identifies relevant temporal segments containing tool-tissue interactions which are spatially localized in the audio-visual scene representation. The system was experimentally evaluated in a realistic operating room setup during simulated surgical procedures performed by experts. Results: The proposed method successfully localizes surgical acoustic events in 3D space and associates them with visual scene elements. Experimental evaluation demonstrates accurate spatial sound localization and robust fusion of multimodal data, providing a comprehensive, dynamic representation of surgical activity. Conclusion: This work introduces the first approach for spatial sound localization in dynamic surgical scenes, marking a significant advancement toward multimodal surgical scene representations. By integrating acoustic and visual data, the proposed framework enables richer contextual understanding and provides a foundation for future intelligent and autonomous surgical systems.

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 / 3 minor

Summary. The manuscript presents a novel framework for creating 4D audio-visual representations of surgical scenes. It integrates 3D acoustic localization using a phased microphone array with dynamic point clouds from an RGB-D camera. A transformer-based module detects acoustic events corresponding to tool-tissue interactions, which are then localized and projected onto the visual point clouds. The approach is evaluated through experiments in a realistic operating room environment during simulated surgical procedures by experts, claiming successful 3D localization and robust multimodal fusion.

Significance. If the results hold with proper validation, this work could advance multimodal surgical scene understanding by incorporating underutilized acoustic data from tool-tissue interactions. The projection of phased-array localizations onto dynamic RGB-D point clouds offers a practical way to enrich contextual modeling beyond visual-only methods, with potential applications in intelligent and autonomous surgical systems.

major comments (2)
  1. [Results] Results section: The claims of 'successful' 3D localization and 'accurate spatial sound localization' are presented without any quantitative metrics (e.g., mean localization error in cm, precision/recall for event detection, or statistical significance), error bars, or baseline comparisons. This directly undermines assessment of the central claim.
  2. [Methods] Methods section: The transformer-based acoustic event detection module lacks specifics on architecture details, training dataset for tool-tissue sounds, hyperparameters, or robustness to OR noise/movement. These omissions are load-bearing for the pipeline's reliability in dynamic scenes.
minor comments (3)
  1. [Abstract] Abstract: The results paragraph would be strengthened by including at least one key quantitative finding to support the success claims.
  2. [Introduction] The novelty claim of being the 'first approach' for spatial sound localization in dynamic surgical scenes requires a dedicated related-work subsection with explicit comparisons to prior audio-visual fusion methods in surgery or robotics.
  3. [Figures] Figure captions for the system overview and projection examples could clarify the coordinate transformations used when mapping acoustic sources onto the dynamic point clouds.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful and constructive review. The comments highlight important areas where additional rigor will strengthen the manuscript. We address each point below and commit to a major revision that incorporates quantitative validation and detailed methodological specifications.

read point-by-point responses

  1. Referee: [Results] Results section: The claims of 'successful' 3D localization and 'accurate spatial sound localization' are presented without any quantitative metrics (e.g., mean localization error in cm, precision/recall for event detection, or statistical significance), error bars, or baseline comparisons. This directly undermines assessment of the central claim.

    Authors: We agree that the current presentation of results is insufficiently quantitative. The revised manuscript will report mean localization error in centimeters with standard deviation, precision and recall for the transformer-based event detection module, error bars across repeated trials, and direct comparisons to baselines including conventional beamforming and visual-only methods. Statistical significance will be assessed using paired t-tests or Wilcoxon tests as appropriate. revision: yes

  2. Referee: [Methods] Methods section: The transformer-based acoustic event detection module lacks specifics on architecture details, training dataset for tool-tissue sounds, hyperparameters, or robustness to OR noise/movement. These omissions are load-bearing for the pipeline's reliability in dynamic scenes.

    Authors: We acknowledge these omissions and will expand the Methods section substantially. The revision will detail the transformer architecture (number of encoder layers, attention heads, and hidden dimensions), the training dataset (size, collection protocol in simulated OR conditions, and annotation process for tool-tissue interactions), all key hyperparameters (learning rate, batch size, optimizer, and training epochs), and dedicated experiments evaluating robustness to realistic OR noise levels and sensor motion. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained system description

full rationale

The paper presents a pipeline for 4D audio-visual surgical scene representation using a phased microphone array for acoustic localization, a transformer module for detecting tool-tissue events, and projection onto RGB-D dynamic point clouds. No mathematical derivations, fitted parameters renamed as predictions, or self-citation chains that reduce the central claim to its own inputs are present in the described methods or results. The evaluation relies on experimental data from simulated procedures in a realistic OR setup, providing external falsifiability independent of any internal definitions. The framework is a coherent integration of established components (phased-array localization, transformer detection, point-cloud projection) without load-bearing steps that collapse by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no equations or implementation details, so no free parameters, axioms, or invented entities can be identified.

pith-pipeline@v0.9.0 · 5785 in / 1117 out tokens · 32881 ms · 2026-05-18T03:28:45.572331+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

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    Relation between the paper passage and the cited Recognition theorem.

    transformer-based acoustic event detection module identifies relevant temporal segments containing tool-tissue interactions which are spatially localized in the audio-visual scene representation... projecting acoustic localization information from a phased microphone array onto dynamic point clouds

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Reference graph

Works this paper leans on

21 extracted references · 21 canonical work pages

  1. [1]

    In: Proc

    Nwoye, C.I., Gonzalez, C., Yu, T., Mascagni, P., Mutter, D., Marescaux, J., Padoy, N.: Recognition of instrument-tissue interactions in endoscopic videos via action triplets. In: Proc. MICCAI 2020, pp. 364–374 (2020)

    work page 2020

  2. [2]

    In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S

    ¨Ozsoy, E., ¨Ornek, E.P., Eck, U., Czempiel, T., Tombari, F., Navab, N.: 4d-or: Semantic scene graphs for or domain modeling. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Proc. MICCAI 2022, pp. 475–485 (2022)

    work page 2022

  3. [3]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp

    ¨Ozsoy, E., Pellegrini, C., Czempiel, T., Tristram, F., Yuan, K., Bani-Harouni, D., Eck, U., Busam, B., Keicher, M., Navab, N.: Mm-or: A large multimodal operating room dataset for semantic understanding of high-intensity surgical environments. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19378–19389 (2025)

    work page 2025

  4. [4]

    https://arxiv.org/abs/2505.24287

    ¨Ozsoy, E., Mamur, A., Tristram, F., Pellegrini, C., Wysocki, M., Busam, B., Navab, N.: EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity Understanding (2025). https://arxiv.org/abs/2505.24287

    work page arXiv 2025

  5. [5]

    Artificial Intelligence Surgery4(3) (2024)

    Ding, H., Seenivasan, L., Killeen, B.D., Cho, S.M., Unberath, M.: Digital twins as a unifying framework for surgical data science: the enabling role of geometric scene understanding. Artificial Intelligence Surgery4(3) (2024)

    work page 2024

  6. [6]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp

    Hein, J., Giraud, F., Calvet, L., Schwarz, A., Cavalcanti, N.A., Prokudin, S., Farshad, M., Tang, S., Pollefeys, M., Carrillo, F., F¨ urnstahl, P.: Creating a digital twin of spinal surgery: A proof of concept. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 2355–2364 (2024)

    work page 2024

  7. [7]

    Nature Biomedical Engineering1, 691–696 (2017)

    Maier-Hein, L., Vedula, S.S., Speidel, S., Navab, N., Kikinis, R., Park, A., Eisen- mann, M., Feussner, H., Forestier, G., Giannarou, S., Hashizume, M., Katic, D., Kenngott, H., Kranzfelder, M., Malpani, A., M¨ arz, K., Neumuth, T., Padoy, N., Pugh, C., Schoch, N., Stoyanov, D., Taylor, R., Wagner, M., Hager, G.D., Jannin, P.: Surgical data science for ne...

    work page 2017

  8. [8]

    In: Proc

    Seibold, M., Hoch, A., Farshad, M., Navab, N., F¨ urnstahl, P.: Conditional gener- ative data augmentation for clinical audio datasets. In: Proc. MICCAI 2022, pp. 345–354 (2022)

    work page 2022

  9. [9]

    Journal of Orthopedic Research (2020)

    Goossens, Q., Pastrav, L., Roosen, J., Mulier, M., Desmet, W., Vander Sloten, J., Denis, K.: Acoustic analysis to monitor implant seating and early detect fractures in cementless tha: An in vivo study. Journal of Orthopedic Research (2020)

    work page 2020

  10. [10]

    Scientific Reports11(2021)

    Seibold, M., Maurer, S., Hoch, A., Zingg, P., Farshad, M., Navab, N., F¨ urnstahl, P.: Real-time acoustic sensing and artificial intelligence for error prevention in 11 orthopedic surgery. Scientific Reports11(2021)

    work page 2021

  11. [11]

    Artificial Intelligence in Medicine144, 102641 (2023)

    Massalimova, A., Timmermans, M., Cavalcanti, N., Suter, D., Seibold, M., Car- rillo, F., Laux, C.J., Sutter, R., Farshad, M., Denis, K., F¨ urnstahl, P.: Automatic breach detection during spine pedicle drilling based on vibroacoustic sensing. Artificial Intelligence in Medicine144, 102641 (2023)

    work page 2023

  12. [12]

    In: Proc

    Seibold, M., Bahari Malayeri, A., Fuernstahl, P.: Spatial context awareness in surgery through sound source localization. In: Proc. MICCAI 2024 (2024)

    work page 2024

  13. [13]

    Innovative Surgical Sciences2(3), 123– 137 (2017)

    Neumuth, T.: Surgical process modeling. Innovative Surgical Sciences2(3), 123– 137 (2017)

    work page 2017

  14. [14]

    Minimally Invasive Therapy & Allied Technologies28(2), 82–90 (2019)

    Padoy, N.: Machine and deep learning for workflow recognition during surgery. Minimally Invasive Therapy & Allied Technologies28(2), 82–90 (2019)

    work page 2019

  15. [15]

    Artif Intell Surg2, 64–79 (2022)

    Wagner, M., Bodenstedt, S., Daum, M., Schulze, A., Younis, R., Brandenburg, J., Kolbinger, F.R., Distler, M., Maier-Hein, L., Weitz, J.,et al.: The importance of machine learning in autonomous actions for surgical decision making. Artif Intell Surg2, 64–79 (2022)

    work page 2022

  16. [16]

    In: Proc

    ¨Ozsoy, E., Czempiel, T., Holm, F., Pellegrini, C., Navab, N.: Labrad-or: Lightweight memory scene graphs for accurate bimodal reasoning in dynamic operating rooms. In: Proc. MICCAI 2023 (2023)

    work page 2023

  17. [17]

    In: Medical Imaging with Deep Learning, vol

    Hamoud, I., Jamal, M.A., Srivastav, V., Mutter, D., Padoy, N., Mohareri, O.: St(or)2: Spatio-temporal object level reasoning for activity recognition in the operating room. In: Medical Imaging with Deep Learning, vol. 227, pp. 1254–1268 (2024)

    work page 2024

  18. [18]

    Medical Image Analysis103, 103613 (2025)

    Hein, J., Cavalcanti, N., Suter, D., Zingg, L., Carrillo, F., Calvet, L., Farshad, M., Navab, N., Pollefeys, M., F¨ urnstahl, P.: Next-generation surgical navigation: Marker-less multi-view 6DoF pose estimation of surgical instruments. Medical Image Analysis103, 103613 (2025)

    work page 2025

  19. [19]

    In Submission(2025)

    Meyer, J., Giraud, F., W¨ uthrich, J., F¨ urnstahl, P., Calvet, L.: Rocsync: Millisecond-accurate temporal synchronization for heterogeneous camera systems. In Submission(2025)

    work page 2025

  20. [20]

    In: Proc

    Gong, Y., Chung, Y.-A., Glass, J.: AST: Audio Spectrogram Transformer. In: Proc. Interspeech 2021, pp. 571–575 (2021)

    work page 2021

  21. [21]

    In: Kdd, vol

    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.,et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, vol. 96, pp. 226–231 (1996) 12

    work page 1996