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REVIEW 4 major objections 4 minor 44 references

An unsupervised workflow turns submarine fiber-optic DAS recordings into fin whale note detections, characterizations, and locations, with median pick-level precision of 0.990.

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2026-08-04 08:08 UTC pith:JY2IA2RC

load-bearing objection Useful integrated DAS workflow for fin whale notes with credible detection numbers, but the localization and movement claims outrun the validation. the 4 major comments →

arxiv 2608.02387 v1 pith:JY2IA2RC submitted 2026-08-03 cs.SD physics.geo-ph

An End-to-End Workflow for Fin Whale Song Detection, Note Characterization, and Localization with Distributed Acoustic Sensing

D\'idac Diego-Tortosa , Miriam Romagosa , Arantza Ugalde , Hugo Latorre , Sergi Ventosa , Jose Enrique Garc\'ia , Antonio Villase\~nor This is my paper
classification cs.SD physics.geo-ph
keywords distributed acoustic sensingfin whalepassive acoustic monitoringKVP pickerhyperbolic localizationnote characterizationsubmarine fiber-optic cable
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper claims that raw distributed acoustic sensing (DAS) data from submarine telecom cables can be converted, without any supervised training, into a compact set of fin whale note detections, each labeled with temporal, spectral, and energy features and assigned a candidate source location. On six manually annotated songs, the method reaches median pick-level precision 0.990 and recall 0.744, and cluster-level precision 0.880 and recall 0.806. It also distinguishes type-A (20-Hz downsweep) from type-B (backbeat) notes and infers apparent swim direction from the sequence of locations. A sympathetic reader would care because DAS arrays are dense and already deployed, so this offers a scalable route to long-term monitoring and song-structure analysis.

Core claim

The central discovery is that a multi-stage signal-processing chain—a kurtosis-based picker adapted with a Morlet wavelet, density-based spatio-temporal clustering, cluster agglomeration, hyperbolic arrival-time fitting, and grid-search localization—can extract biologically interpretable note-level information from raw DAS strain records. The hyperbolic fitting rejects incoherent picks, yielding validated clusters whose features support note-type discrimination, and whose relative arrival times produce matching-score maps of the source. The evaluation against manual annotations shows that the method is both precise and transferable across two different cable geometries.

What carries the argument

The key machinery is the hyperbolic arrival-time model t(d)^2 = t0^2 + (d-d0)^2/v_app^2, which links each note to a localized source and drives both the rejection of incoherent picks and the grid-search localization. Around it sit the kurtosis-value picker (KVP) with a Morlet wavelet for narrow-band detection, DBSCAN plus hierarchical agglomeration to group channel-wise picks, and a spectrogram-based feature extractor producing centroids, spreads, slopes, and SNR. The matching score M = 1/(1+epsilon^2) converts arrival-time residuals into a spatial map of candidate source positions.

Load-bearing premise

The load-bearing premise is that every fin whale note generates a single hyperbolic arrival-time pattern on the DAS array with a constant apparent velocity; if multipath propagation, depth changes, or variable cable coupling break that hyperbola, the clustering validation and localization results become unreliable.

What would settle it

A controlled test would be to record a fin whale with a collocated hydrophone array and a DAS cable, localize the caller with the hydrophones, and check whether the DAS-derived matching-score peak coincides with the true location; if the peak is systematically offset or the hyperbola residuals are large in a sizable fraction of notes, the central claim is weakened.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • DAS-equipped submarine cables can serve as continuous passive acoustic monitors for fin whales without labelled training data.
  • Note-level features enable estimation of inter-note intervals for AA, AB, BA, BB sequences, allowing song-structure and population studies.
  • Sequential localization of notes yields apparent swimming speed and direction, with estimates in this paper around 3.3–6.5 km/h, consistent with known behavior.
  • The workflow is transferable to hydrophone arrays, cabled observatories, or OBS networks with minor adaptation.
  • Overlapping vocalizations from two individuals can be separated when their hyperbola apices differ, as demonstrated on a 15 January 2024 recording.

Where Pith is reading between the lines

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

  • If the hyperbolic assumption holds, the same pipeline could track individual whales over longer timescales by linking cluster apices, a step the paper leaves for future work.
  • The matching-score maps, though not calibrated uncertainties, could be used to design better cable geometries (curved or multi-cable) to remove the bilateral ambiguity—an extension hinted but not pursued.
  • The feature set might generalize to other pulsed low-frequency tonal calls (e.g., sei or Bryde's whales) if the wavelet scale and frequency band are adjusted; this is an untested extrapolation.
  • A natural stress test is to apply the workflow to a dataset with known ground-truth positions (e.g., tagged whales) to calibrate localization accuracy, which the current evaluation does not do.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper presents an end-to-end, unsupervised workflow for detecting, characterizing, and localizing fin whale notes in distributed acoustic sensing (DAS) data from two submarine telecom cables. The pipeline adapts the kurtosis-value picker (KVP) with a Morlet wavelet for narrow-band note detection, groups channel-wise picks via DBSCAN and agglomeration, fits a hyperbolic moveout model to reject outliers, extracts temporal/spectral/energy features for note typing, and applies a grid-search relative-arrival-time method to produce matching-score maps for source localization. Detection is evaluated against manual annotations from six fin whale songs, reporting median pick-level precision 0.990 and recall 0.744, and cluster-level precision 0.880 and recall 0.806. Representative examples illustrate overlapping-song separation, type-A/B characterization, and inference of apparent source movement at speeds of 3.31 and 6.48 km/h.

Significance. If the results hold, the workflow is a useful contribution to DAS-based marine mammal monitoring: it is fully unsupervised, interpretable, open-source, and demonstrated on two real-world cable deployments with contrasting geometry and noise conditions. A notable strength is that the detection stage does not require labeled training data, and the code is publicly available. The paper also correctly limits some claims, noting that score maps are not calibrated uncertainty. However, the two most consequential claims—transferable detection performance and localization/movement inference—currently rest on thinner evidence: the metrics are computed on the same data used to tune the pipeline, and the localization stage has no independent validation. These gaps need to be addressed before the paper's conclusions can be taken at face value.

major comments (4)
  1. [Sec. 4.1 and Table 1] The detection metrics (median pick precision 0.990, recall 0.744; cluster precision 0.880, recall 0.806) are reported on the same six songs that were presumably used to develop and tune the KVP settings and clustering parameters. There is no held-out set or cross-validation, so the reported numbers likely overestimate performance on new recordings. This is load-bearing for the paper's 'transferability' claim over two datasets. Please provide an explicit train/test split or cross-validation, or clearly state which parameters were fixed a priori and which were tuned on these six songs.
  2. [Sec. 3.2 (DBSCAN and agglomeration parameters)] The spatio-temporal clustering stage depends on several parameters that are not reported: DBSCAN neighborhood radius eps, min_samples, temporal/spatial agglomeration margins, and the hyperbolic-fit outlier residual threshold. Table 1 lists only the KVP/Morlet settings. Without these values (or a pointer to a configuration file in the GitHub repository), the workflow cannot be reproduced or adapted to other DAS arrays. Please include a complete parameter table or cite the exact configuration in the open-source code.
  3. [Sec. 4.3 and Sec. 5.3 (localization and movement inference)] The abstract claims 'inference of apparent source movement,' but the localization results are not validated against any independent ground truth, synthetic test, or known source position. The method assumes a constant sound speed of 1500 m/s, a fixed source depth, and a horizontal propagation path (Sec. 3.4), and the clustering/outlier rejection already assumes a single-hyperbola moveout with constant v_app (Eq. 2). The paper's own Sec. 5.3 states that score maps 'should not be interpreted as calibrated localization uncertainty.' Without validation, the reported swimming speeds of 3.31 and 6.48 km/h could be artifacts of the assumed propagation model rather than true whale motion. Please add a quantitative validation (e.g., synthetic waveforms, a known source, or a comparison with independent tracking) or explicitly reframe the movement claims as model-dependent 'apparent' displacements
  4. [Sec. 3.2, Eq. (2) and Sec. 2 (bathymetry)] The hyperbolic moveout model in Eq. (2) is central to both clustering and localization, but the cables are not straight: Fig. 1 shows bathymetric variations and curvature. If the along-fiber distance d differs from the straight-line distance to the source, the arrival-time pattern may deviate from a pure hyperbola, affecting both outlier rejection and the grid-search localization. The paper does not quantify this error or test the sensitivity of clustering/localization to cable geometry. Please add a sensitivity analysis or at least a clear statement of the geometric assumptions and their expected impact on the reported results.
minor comments (4)
  1. [Sec. 3.1] Typo: 'KPV' should be 'KVP' in the second paragraph ('In the present study, KPV was adapted').
  2. [Sec. 3.3] Typo: 'spectrogram chacracteristics' -> 'spectrogram characteristics'; also 'descrptors' in Sec. 4.2 and 'reatining' in Sec. 5.1.
  3. [Sec. 5.3 / Fig. 8 caption] The phrase 'adirectly' in Sec. 5.3 is a typo, and Fig. 8 caption contains '99 th percentile os each' — should be 'of each'.
  4. [Sec. 3.3, Eq. (6)] The dimensionless factor σ is set to 1.5, which is described as defining the signal support. It would help to state explicitly that this value was chosen by visual inspection and whether the features are sensitive to it.

Circularity Check

0 steps flagged

No significant circularity; detection metrics are benchmarked against manual annotations and the localization model's limits are explicitly stated.

full rationale

The derivation chain is not circular. The detection stage (KVP adapted with a Morlet wavelet) is evaluated against manually annotated KVP picks from six fin whale songs, yielding precision/recall figures that are independent of the algorithm's own outputs. The clustering and hyperbolic fitting around Eq. 2 are used to reject incoherent picks, and the retained clusters are subsequently fed into note characterization and localization; no parameter is fitted and then reported as an independent prediction of itself. The localization step (Sec. 3.4, Eqs. 7-8) is an explicit grid-search inversion under stated assumptions (fixed source depth, constant 1500 m/s sound speed), and the paper itself cautions that the matching-score maps 'should not be interpreted as calibrated localization uncertainty.' Thus the apparent-movement inference is a model-dependent interpretation, not a circular derivation. Self-citations to Latorre et al. 2025 (KVP) and Diego-Tortosa et al. 2025 (spectrogram configurations) provide algorithms or standard time-frequency trade-offs; they are not used as unverified premises to force the paper's conclusions, and the main detection claim is externally benchmarked. The principal weakness, lack of independent ground-truth positions for localization, is a validation gap rather than a circularity.

Axiom & Free-Parameter Ledger

9 free parameters · 6 axioms · 0 invented entities

The workflow is a combination of existing algorithms; its free parameters are numerous and mostly hand-tuned, with several key values undisclosed in the manuscript, making the reported performance dependent on unpublished choices.

free parameters (9)
  • KVP wavelet and detection settings (max frequency, octaves, voices, center cycles, jump threshold, min gap, n bands, Mor = 25 Hz, 1 octave, 10 voices/oct, 150.0, 2.0, 1.0 s, 22, 5.3364
    Hand-set in Table 1 for fin whale notes; no sensitivity analysis.
  • Frequency selection band limits = 15–30 Hz, max 4 bands
    Chosen based on known fin whale call frequencies; excludes picks outside.
  • DBSCAN neighborhood radius (eps)
    Not specified numerically in the paper; required for clustering.
  • DBSCAN min_samples
    Not specified; required for clustering.
  • Cluster agglomeration margins
    Predefined temporal and spatial margins not given.
  • Hyperbolic fit outlier residual threshold
    Threshold for removing picks after initial fit not specified.
  • Localization grid extent and spacing = 6x6 km, 25 m
    Chosen by authors; affects resolution and runtime.
  • Assumed sound speed and source depth = 1500 m/s, upper 50 m
    Domain assumptions for travel-time computation.
  • Feature extraction sigma = 1.5
    Dimensionless factor defining temporal/spectral spread support.
axioms (6)
  • domain assumption Hyperbolic arrival-time model (Eq. 2) holds for fin whale notes on a linear DAS array.
    Assumes homogeneous medium and point source; used for clustering and localization.
  • domain assumption Constant sound speed of 1500 m/s in seawater.
    Used in grid-search localization; ignores spatial variability.
  • domain assumption Fin whale notes are narrow-band between 15 and 30 Hz.
    From literature (Sciacca 2015); used for frequency selection.
  • domain assumption Fin whales vocalize from the upper 50 m of the water column.
    Cited from Watkins 1987, Stimpert 2015; allows 2D localization.
  • standard math KVP and DBSCAN algorithms function as described in their source papers.
    The pipeline relies on these published algorithms.
  • ad hoc to paper Manual annotations of six songs are correct ground truth.
    Annotations are author-created; no inter-annotator agreement reported.

pith-pipeline@v1.3.0-daily-deepseek · 13018 in / 10317 out tokens · 95937 ms · 2026-08-04T08:08:53.716779+00:00 · methodology

0 comments
read the original abstract

Submarine fiber-optic cables instrumented with distributed acoustic sensing (DAS) provide an effective approach for large-scale monitoring of fin whales. We present an end-to-end workflow for detecting, characterizing, and localizing fin whale notes, tested on two submarine telecom cables in the Strait of Gibraltar and western Alboran Sea. The workflow applies a kurtosis-value picker adapted to narrow-band fin whale notes. Channel-wise detections are grouped into individual notes using density-based spatio-temporal clustering, cluster agglomeration, and hyperbolic fitting to reject incoherent picks. The retained clusters are characterized through temporal, spectral, and energy-related descriptors that support note-type discrimination and estimation of inter-note intervals. Relative arrival times across DAS channels are then used in a grid-search procedure to estimate candidate source locations. Evaluation against manually annotated detections from six fin whale songs yielded median pick-level precision of 0.990 and recall of 0.744, and median cluster-level precision of 0.880 and recall of 0.806. Representative applications demonstrate separation of overlapping vocalizations, characterization of type-A and type-B notes, and the inference of apparent source movement. By transforming dense DAS recordings into compact note-level bioacoustic information, the workflow provides an integrated framework for fin whale monitoring and a basis for adaptation to other synchronized acoustic receiver arrays.

Figures

Figures reproduced from arXiv: 2608.02387 by Antonio Villase\~nor, Arantza Ugalde, D\'idac Diego-Tortosa, Hugo Latorre, Jose Enrique Garc\'ia, Miriam Romagosa, Sergi Ventosa.

Figure 1
Figure 1. Figure 1: Location of the two experimental sites used in this study. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Frequency response of an octave of 12 voices for both [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: a shows the 175 039 raw KVP picks generated from the selected recording. After application of the frequency [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 6
Figure 6. Figure 6: b shows contours corresponding to the 99 [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 6
Figure 6. Figure 6: Representative localization results for a fin whale song recorded on the Estepona cable beginning on 13 [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Representative sequence of 78 consecutive clusters corresponding to two temporally interleaved fin whale [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Representative localization results for a fin whale song recorded on the Estepona cable on 14 October 2023, [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

44 extracted references · 29 canonical work pages · 1 internal anchor

  1. [1]

    Idrissi, Abdelghani and Bonanno, Danilo and Di Mauro, Letizia S. and Diego-Tortosa, Dídac and Gómez-García, Clara and Ker, Stephan and Le Pape, Florian and Murphy, Shane and Pulvirenti, Sara and Riccobene, Giorgio and Sanfilippo, Simone and Viola, Salvatore , year =. Detection of Seismic and Acoustic Sources Using Distributed Acoustic Sensing Technology i...

    doi:10.3390/jmse13040658

  2. [2]

    and Nieukirk, S

    Romagosa, M. and Nieukirk, S. and Cascão, I. and Marques, T. A. and Dziak, R. and Royer, J.-Y. and O'Brien, J. and Mellinger, D. K. and Pereira, A. and Ugalde, A. and Papale, E. and Aniceto, S. and Buscaino, G. and Rasmussen, M. and Matias, L. and Prieto, R. and Silva, M. A. , year =. Fin whale song evolution in the. doi:10.7554/elife.83750 , journal =

    work page doi:10.7554/elife.83750

  3. [3]

    Goestchel, Quentin and Wilcock, William S. D. and Abadi, Shima , journal =. Automated association of fin whale calls for localization using distributed acoustic sensing , year =. doi:10.1121/10.0044257 , publisher =

    work page doi:10.1121/10.0044257

  4. [4]

    Automated detection of fin whales with distributed acoustic sensing in the Arctic and Mediterranean , year =

    Truong, Khanh and Eidsvik, Jo and Rørstadbotnen, Robin Andre and Morten, Jan Petter and Andres, Laurine and Sladen, Anthony , journal =. Automated detection of fin whales with distributed acoustic sensing in the Arctic and Mediterranean , year =. doi:10.1121/10.0041855 , publisher =

    doi:10.1121/10.0041855

  5. [5]

    Animal Biotelemetry , publisher =

    Stimpert, Alison K and DeRuiter, Stacy L and Falcone, Erin A and Joseph, John and Douglas, Annie B and Moretti, David J and Friedlaender, Ari S and Calambokidis, John and Gailey, Glenn and Tyack, Peter L and Goldbogen, Jeremy A , year =. Animal Biotelemetry , publisher =. doi:10.1186/s40317-015-0058-3 , number =

    work page doi:10.1186/s40317-015-0058-3

  6. [6]

    Goestchel, Quentin and Wilcock, William S. D. and Abadi, Shima , journal =. Enhancing fin whale vocalizations in distributed acoustic sensing data , year =. doi:10.1121/10.0036696 , publisher =

    work page doi:10.1121/10.0036696

  7. [7]

    Detection of hydroacoustic signals on a fiber-optic submarine cable , volume =

    Matsumoto, Hiroyuki and Araki, Eiichiro and Kimura, Toshinori and Fujie, Gou and Shiraishi, Kazuya and Tonegawa, Takashi and Obana, Koichiro and Arai, Ryuta and Kaiho, Yuka and Nakamura, Yasuyuki and Yokobiki, Takashi and Kodaira, Shuichi and Takahashi, Narumi and Ellwood, Robert and Yartsev, Victor and Karrenbach, Martin , year =. Detection of hydroacous...

    work page doi:10.1038/s41598-021-82093-8

  8. [8]

    and Ugalde, A

    Seguí, A. and Ugalde, A. and Fichtner, A. and Ventosa, S. and Morros, J.R. , year =. Geophysical Journal International , publisher =. doi:10.1093/gji/ggaf397 , number =

    doi:10.1093/gji/ggaf397

  9. [9]

    Research Progress in Distributed Acoustic Sensing Techniques , volume =

    Shang, Ying and Sun, Maocheng and Wang, Chen and Yang, Jian and Du, Yuankai and Yi, Jichao and Zhao, Wenan and Wang, Yingying and Zhao, Yanjie and Ni, Jiasheng , year =. Research Progress in Distributed Acoustic Sensing Techniques , volume =. Sensors , publisher =. doi:10.3390/s22166060 , number =

    doi:10.3390/s22166060

  10. [10]

    , year =

    Hartog, Arthur H. , year =. An Introduction to Distributed Optical Fibre Sensors , ISBN =. doi:10.1201/9781315119014 , publisher =

    doi:10.1201/9781315119014

  11. [11]

    and Feakes, Amy M

    Edwards, Elodie A. and Feakes, Amy M. and Olcay, Abdullah A. and Minshull, Timothy A. , year =. Seismic survey noise reduces fin whale vocalisations offshore northwestern Spain , volume =. Scientific Reports , publisher =. doi:10.1038/s41598-026-40903-x , number =

    work page doi:10.1038/s41598-026-40903-x

  12. [12]

    Fin whale song recordings by onshore seismometers open new horizons for cetacean coastal monitoring , volume =

    M\". Fin whale song recordings by onshore seismometers open new horizons for cetacean coastal monitoring , volume =. Scientific Reports , publisher =. doi:10.1038/s41598-025-97638-4 , number =

    work page doi:10.1038/s41598-025-97638-4

  13. [13]

    Fin Whale (Balaenoptera physalus) Migration in the Strait of Gibraltar: Evaluating Maritime Traffic Threats and Conservation Measures , volume =

    Espada, Rocío and Olaya-Ponzone, Liliana and Martín-Moreno, Estefania and Gil-Vera, Paco and Anfruns Fernández, Iris and Patón Domínguez, Daniel and García-Gómez, José Carlos , year =. Fin Whale (Balaenoptera physalus) Migration in the Strait of Gibraltar: Evaluating Maritime Traffic Threats and Conservation Measures , volume =. Journal of Marine Science ...

    work page doi:10.3390/jmse13112156

  14. [14]

    Contemporary migration of fin whales through the Strait of Gibraltar , volume =

    Gauffier, P and Verborgh, P and Giménez, J and Esteban, R and Salazar Sierra, JM and de Stephanis, R , year =. Contemporary migration of fin whales through the Strait of Gibraltar , volume =. doi:10.3354/meps12449 , journal =

    work page doi:10.3354/meps12449

  15. [15]

    and Abadi, Shima and Lipovsky, Bradley P

    Douglass, Alexander S. and Abadi, Shima and Lipovsky, Bradley P. , year =. Distributed acoustic sensing for detecting near surface hydroacoustic signals , volume =. JASA Express Letters , publisher =. doi:10.1121/10.0019703 , number =

    work page doi:10.1121/10.0019703

  16. [16]

    Test experiments with distributed acoustic sensing and hydrophone arrays for locating underwater sound sources

    Test experiments with distributed acoustic sensing and hydrophone arrays for locating underwater sound sources , publisher =. 2023 , copyright =. doi:10.48550/ARXIV.2306.04276 , author =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2306.04276 2023

  17. [17]

    Journal of Marine Science and Engineering , publisher =

    Di Mauro, Letizia Stella and Diego-Tortosa, Dídac and Sciacca, Virginia and Riccobene, Giorgio and Viola, Salvatore , year =. Journal of Marine Science and Engineering , publisher =. doi:10.3390/jmse13040682 , number =

    work page doi:10.3390/jmse13040682

  18. [18]

    New Insights into the Design and Application of a Passive Acoustic Monitoring System for the Assessment of the Good Environmental Status in Spanish Marine Waters , volume =

    Lara, Guillermo and Miralles, Ramón and Bou-Cabo, Manuel and Esteban, José Antonio and Espinosa, Víctor , year =. New Insights into the Design and Application of a Passive Acoustic Monitoring System for the Assessment of the Good Environmental Status in Spanish Marine Waters , volume =. Sensors , publisher =. doi:10.3390/s20185353 , number =

    work page doi:10.3390/s20185353

  19. [19]

    and Ng, Tien Khee and Duarte, Carlos M

    Ariff, Talha and Havlik, Michelle-Nicole and Kang, Chun Hong and Rjeb, Alaaeddine and Marin, Juan M. and Ng, Tien Khee and Duarte, Carlos M. and Ashry, Islam and Ooi, Boon S. , year =. Marine reef soundscape monitoring with fiber-optic distributed acoustic sensing , volume =. Scientific Reports , publisher =. doi:10.1038/s41598-025-30200-4 , number =

    work page doi:10.1038/s41598-025-30200-4

  20. [20]

    Simultaneous tracking of multiple whales using two fiber-optic cables in the Arctic , volume =

    Rørstadbotnen, Robin André and Eidsvik, Jo and Bouffaut, Léa and Landrø, Martin and Potter, John and Taweesintananon, Kittinat and Johansen, Ståle and Storevik, Frode and Jacobsen, Joacim and Schjelderup, Olaf and Wienecke, Susann and Johansen, Tor Arne and Ruud, Bent Ole and Wuestefeld, Andreas and Oye, Volker , year =. Simultaneous tracking of multiple ...

    arXiv 2023

  21. [21]

    and Gagnon, George J

    Clark, Christopher W. and Gagnon, George J. and Frankel, Adam S. , year =. Fin whale singing decreases with increased swimming speed , volume =. Royal Society Open Science , publisher =. doi:10.1098/rsos.180525 , number =

    work page doi:10.1098/rsos.180525

  22. [22]

    Combination of the Two: Advancing Marine Soundscape Monitoring Using

    Grenga, Flavia and Tricomi, Alessia and Gómez-García, Clara and Diego-Tortosa, Dídac and Geraci, Elena and Riccobene, Giorgio and Viola, Salvatore , year =. Combination of the Two: Advancing Marine Soundscape Monitoring Using. doi:10.1007/978-3-031-94229-7_181-1 , booktitle =

    work page doi:10.1007/978-3-031-94229-7_181-1

  23. [23]

    Watkins, W. A. and Tyack, P. and Moore, K. E. and Bird, J. E. , year =. The 20-. The Journal of the Acoustical Society of America , publisher =. doi:10.1121/1.395685 , number =

    work page doi:10.1121/1.395685

  24. [24]

    ESS Open Archive , year =

    Bouffaut, Léa , title =. ESS Open Archive , year =. doi:10.22541/essoar.15005355/v1 , url =

    work page doi:10.22541/essoar.15005355/v1

  25. [25]

    Edds-Walton, P. L. , title =. Bioacoustics , publisher =. 1997 , month = jan, pages =. doi:10.1080/09524622.1997.9753353 , number =

    arXiv 1997

  26. [26]

    and Cusí, J

    Gaspà Rebull, O. and Cusí, J. D. and Ruiz Fernández, M. and Muset, J. G. , year =. Tracking. The Journal of the Acoustical Society of America , publisher =. doi:10.1121/1.2336751 , number =

    work page doi:10.1121/1.2336751

  27. [27]

    and Stanek, Frantisek , journal =

    Horne, Steve and Stork, Anna L. and Stanek, Frantisek , journal =. Observations of fin whales and vessels offshore oregon using fibre optic distributed acoustic sensing , year =. doi:10.3389/fmars.2025.1603541 , file =

    arXiv 2025

  28. [28]

    Seismological Research Letters , publisher =

    Woollam, Jack and Rietbrock, Andreas and Leitloff, Jens and Hinz, Stefan , year =. Seismological Research Letters , publisher =. doi:10.1785/0220200037 , number =

    work page doi:10.1785/0220200037

  29. [29]

    State of Play in Marine Soundscape Assessments , volume =

    Havlik, Michelle-Nicole and Predragovic, Milica and Duarte, Carlos Manuel , year =. State of Play in Marine Soundscape Assessments , volume =. doi:10.3389/fmars.2022.919418 , journal =

    arXiv 2022

  30. [30]

    and Putland, Rosalyn L

    Merchant, Nathan D. and Putland, Rosalyn L. and André, Michel and Baudin, Eric and Felli, Mario and Slabbekoorn, Hans and Dekeling, René , year =. A decade of underwater noise research in support of the European Marine Strategy Framework Directive , volume =. doi:10.1016/j.ocecoaman.2022.106299 , journal =

    arXiv 2022

  31. [31]

    and Thomas, Len and Martin, Stephen W

    Marques, Tiago A. and Thomas, Len and Martin, Stephen W. and Mellinger, David K. and Ward, Jessica A. and Moretti, David J. and Harris, Danielle and Tyack, Peter L. , year =. Estimating animal population density using passive acoustics , volume =. Biological Reviews , publisher =. doi:10.1111/brv.12001 , number =

    work page doi:10.1111/brv.12001

  32. [32]

    Management and research applications of real-time and archival passive acoustic sensors over varying temporal and spatial scales , volume =

    Van Parijs, SM and Clark, CW and Sousa-Lima, RS and Parks, SE and Rankin, S and Risch, D and Van Opzeeland, IC , year =. Management and research applications of real-time and archival passive acoustic sensors over varying temporal and spatial scales , volume =. doi:10.3354/meps08123 , journal =

    work page doi:10.3354/meps08123

  33. [33]

    and Morten, Jan Petter and Oye, Volker and Bjørnstad, Steinar , year =

    Baird, Alan F. and Morten, Jan Petter and Oye, Volker and Bjørnstad, Steinar , year =. Ocean Space Surveillance and Real-Time Event Characterization Using Distributed Acoustic Sensing on Submarine Networks , volume =. Seismological Research Letters , publisher =. doi:10.1785/0220240360 , number =

    work page doi:10.1785/0220240360

  34. [34]

    Temporal evolution of the Mediterranean fin whale song , year =

    Best, Paul and Marxer, Ricard and Paris, Sébastien and Glotin, Hervé , journal =. Temporal evolution of the Mediterranean fin whale song , year =. doi:10.1038/s41598-022-15379-0 , publisher =

    work page doi:10.1038/s41598-022-15379-0

  35. [35]

    and Bonanno, D

    Diego-Tortosa, D. and Bonanno, D. and Bou-Cabo, M. and Di Mauro, L. S. and Idrissi, A. and Lara, G. and Riccobene, G. and Sanfilippo, S. and Viola, S. , title =. Journal of Marine Science and Engineering , publisher =. doi:10.3390/jmse13030454 , number =

    doi:10.3390/jmse13030454

  36. [36]

    and Caruso, F

    Sciacca, V. and Caruso, F. and Beranzoli, L. and Chierici, F. and De Domenico, E. and Embriaco, D. and Favali, P. and Giovanetti, G. and Larosa, G. and Marinaro, G. and Papale, E. and Pavan, G. and Pellegrino, C. and Pulvirenti, S. and Simeone, F. and Viola, S. and Riccobene, G. , title =. PLOS ONE , year =

  37. [37]

    Weirathmueller, M. J. and Stafford, K. M. and Wilcock, W. S. D. and Hilmo, R. S. and Dziak, R. P. and Tréhu, A. M. , title =. PLOS ONE , year =

  38. [38]

    and Ventosa, S

    Latorre, H. and Ventosa, S. and Ugalde, A. and Villaseñor, A. and Bartolomé, R. and Ranero, C. R. , year =. Geophysical Journal International , publisher =. doi:10.1093/gji/ggaf136 , number =

    work page doi:10.1093/gji/ggaf136

  39. [39]

    and Lammers, Marc O

    Castellote, Manuel and Clark, Christopher W. and Lammers, Marc O. , year =. Fin whale (. Marine Mammal Science , publisher =. doi:10.1111/j.1748-7692.2011.00491.x , number =

    arXiv 2011

  40. [40]

    and Lammers, Marc O

    Castellote, Manuel and Clark, Christopher W. and Lammers, Marc O. , year =. Acoustic and behavioural changes by fin whales (Balaenoptera physalus) in response to shipping and airgun noise , volume =. Biological Conservation , publisher =. doi:10.1016/j.biocon.2011.12.021 , number =

    work page doi:10.1016/j.biocon.2011.12.021 2011

  41. [41]

    and Ventosa, S

    Latorre, H. and Ventosa, S. and Diego-Tortosa, D. and Ugalde, A. , title =. Galileo Conference: Fibre Optic Sensing in Geosciences , publisher =. 2026 , address =. doi:10.5194/egusphere-gc14-fibreoptic-63 , note =

    work page doi:10.5194/egusphere-gc14-fibreoptic-63 2026

  42. [42]

    and Kriegel, H.-P

    Ester, M. and Kriegel, H.-P. and Sander, J. and Xu, X. , title =. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining , pages =. 1996 , publisher =

    1996

  43. [43]

    and Bartolome, R

    Ugalde, A. and Bartolome, R. and Cabieces, R. and Grevemeyer, I. and Villaseñor, A. and Estrada, F. and Palomino, D. and Cubas, M. and Neri, A. and Latorre, H. , title =. doi:10.7914/ZGE4-KD84 , url =

    work page doi:10.7914/zge4-kd84

  44. [44]

    and Martín-López, S

    Ugalde, A. and Martín-López, S. and González-Herraez, M. , title =. doi:10.7914/QMCG-2X78 , url =

    work page doi:10.7914/qmcg-2x78