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SQLite database to accompany the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring"
<p>This dataset is a SQLite database that accompanies methods and analysis described in the paper, "Statistical learning mitigation of false positives from template-detected data in automated acoustic wildlife monitoring" (Balantic & Donovan 2019, Bioacoustics, https://www.tandfonline.com/doi/full/10.1080/09524622.2019.1605309). </p> <p>A Github repository containing code for using the SQLite database also accompanies this paper at: <a href="https://github.com/cbalantic/false-positive-mitigation">http://github.com/cbalantic/false-positive-mitigation</a></p>
DAS4Whale: Svalbard distributed acoustic sensing dataset for baleen whale monitoring
<p> </p> <p> </p> <p>This dataset aims to support the work presented in</p> <blockquote> <p>Bouffaut, L., Taweesintananon, K., Kriesell, H. J., Rørstadbotnen, R. A., Potter, J. R., Landrø, M., Johansen, S. E., Brenne, J. K., Haukanes, A., Schjelderup, O., & Storvik, F. (2022). Eavesdropping at the Speed of Light: Distributed Acoustic Sensing of Baleen Whales in the Arctic. Frontiers in Marine Science, 9, 901348. <a href="https://doi.org/10.3389/fmars.2022.901348">https://doi.org/10.3389/fmars.2022.901348</a>.</p> </blockquote> <p>It contains recordings from a dark fiber optic (FO) cable converted into a distributed acoustic sensing (DAS) array of 120km long spreading from Longyearbyen, Svalbard, Norway, out to the open ocean, through Isfjorden. <a href="https://www.frontiersin.org/files/Articles/901348/fmars-09-901348-HTML/image_m/fmars-09-901348-g002.jpg">This DAS array</a>, measuring nano strain, was spatially sampled every ~4m and had a sampling frequency of 645.16 Hz, generating data stored into spatio-temporal matrices. </p> <p>The exact position of the FO cable is proprietary information belonging to Uninett. The space component is therefore given as a vector in “channel number” (sensing node number along the FO cable) and distance from the shore station (m).</p> <p>The data necessary to produce each manuscript example is saved into multiple files corresponding to subsequent groups of channels along the FO cable, to facilitate storage and sharing. The file naming system satisfies the following: Date in the format <em>YYYYMMDD</em>, UTC time at the beginning of the file, channels, whale_raw, duration of the file L<em>xx</em>s, all separated by underscores “_”. Data is shared as *.mat file saved in HDF format and readable in different programming languages. For example </p> <ul> <li>in <a href="https://www.mathworks.com/help/matlab/ref/load.html">Matlab</a> <pre><code>load('20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat')</code></pre> <p> </p> </li> </ul> <ul> <li>in <a href="http://https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html#scipy.io.loadmat">Python</a> <pre><code>scipy.io.loadmat('20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat')</code></pre> <p> </p> </li> </ul> <p><strong>Each file contains the following variables</strong></p> <ul> <li><em>data: </em>The DAS-recorded nano strain data</li> <li><em>info_GL_m:</em> Used gauge length (m)</li> <li><em>info_nsamples</em>: Number of temporal samples in the file</li> <li><em>info_ntraces</em>: Number of spatial samples (channels) in the file</li> <li><em>info_sample_interval_s</em>: Sampling period (s)</li> <li><em>info_sampling_frequency_Hz</em>: Sampling frequency (Hz)</li> <li><em>info_SSI_m</em>: Spatial sampling interval (m)</li> <li><em>info_timestamp</em>: Date and time (UTC) of the first sample</li> <li>info_units: Global unit information</li> <li><em>x1_absolute_channel</em>: Vector containing the absolute channel number</li> <li><em>x1_distance_from_shore_m</em>: Vector containing the distance along the FO cable from shore (m)</li> <li><em>x1_position_m</em>: Vector containing the distance along the FO cable from the interrogator (m)</li> <li><em>x1_recwdepthz_m</em>: Vector containing the water column depth used as a proxy for the fiber optic cable depth at each sensing location (m)</li> <li><em>x1_relative_channel</em>: Vector containing the channel number</li> <li><em>x2_time_s</em>: Time vector (s)</li> </ul> <p> </p> <p><strong>List of the files and related manuscript examples</strong></p> <p>Example of at least 3 vocalizing baleen whales recorded simultaneously at three different locations along the Svalbard fiber optic DAS array - Figure 4 in Bouffaut et al. (2022) - between 35-95 km and on 2020-06-26 between 052440-052720 UTC</p> <ul> <li><em>20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat</em> </li> <li><em>20200627_052441_ch10001_to_ch15000_whale_raw_L160s.mat</em> </li> <li><em>20200627_052441_ch15001_to_ch20000_whale_raw_L160s.mat</em> </li> <li><em>20200627_052441_ch20001_to_ch25000_whale_raw_L160s.mat</em> </li> </ul> <p>Example of<strong> </strong>series of blue whale calls recorded with a move out on the Svalbard DAS array - Figure 5 & &B in Bouffaut et al. (2022) - between 85-90 km and on 2020-07-16 between 154300-155500 UTC</p> <ul> <li><em>20200716_154302_ch20001_to_ch21000_whale_raw_L720s.mat </em></li> <li><em>20200716_154302_ch21001_to_ch22000_whale_raw_L720s.mat </em></li> <li><em>20200716_154302_ch22001_to_ch23000_whale_raw_L720s.mat</em></li> <li><em>20200716_154302_ch23001_to_ch24000_whale_raw_L720s.mat</em></li> <li><em>20200716_154302_ch24001_to_ch25000_whale_raw_L720s.mat</em></li> </ul> <p>Example of a blue whale non-stereotyped call recorded inside Isfjorden and further used to provide correlated seismic profiles - Figure 6A n Bouffaut et al. (2022) - between 23-28 km on 2020-06-27 between 192255-192805 UTC</p> <ul> <li><em>20200627_192255_ch05001_to_ch07000_whale_raw_L310s.mat </em></li> <li><em>20200627_192255_ch07001_to_ch08500_whale_raw_L310s.mat </em></li> </ul> <p><strong>--------------</strong></p> <p><strong>Analysis tools </strong></p> <p>To reproduce the paper's result, we suggest using the following Python package available on <a href="https://github.com/leabouffaut/DAS4Whales">GitHub</a>:</p> <blockquote> <p>Léa Bouffaut (2023). DAS4Whales: A Python package to analyze Distributed Acoustic Sensing (DAS) data for marine bioacoustics (v0.1.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.7760187">https://doi.org/10.5281/zenodo.7760187</a></p> </blockquote> <p>Here is an example of the use of the DAS4Whales package with this dataset's data format: <a href="https://gist.github.com/leabouffaut/b42ec74e2cee880877bfc4c94e81bdaa">https://gist.github.com/leabouffaut/b42ec74e2cee880877bfc4c94e81bdaa</a></p> <p><strong>--------------</strong></p> <p><strong>Please cite as </strong></p> <blockquote> <p>Léa Bouffaut and Kittinat Taweesintananon, “DAS4Whale: Svalbard distributed acoustic sensing dataset for baleen whale monitoring”. Zenodo, Jan. 10, 2022. doi: <a href="https://doi.org/10.5281/zenodo.7760187">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.5823343">10.5281/zenodo.5823343</a>.</p> </blockquote> <p><strong>--------------</strong></p> <p><strong>Contact</strong></p> <p><a href="mailto:lb736@cornell.edu">Contact</a> | <a href="https://www.birds.cornell.edu/ccb/lea-bouffaut/">Webpage</a> | <a href="https://twitter.com/LeaBouffaut">Twitter</a></p>
Rainfall data monitored by acoustic sensors in Zurich and Milan during spring and summer 2022
<p>The database contains rainfall information obtained from acoustic sensors and rain gauges (meteoblue AG) in the cities of Zurich (Switzerland) and Milan (Italy) during field work conducted in spring and summer 2022.</p> <p>Zurich:</p> <p>Continuous rainfall data is provided at 15 min intervals for April 2022; data_acoustic_Zurich.csv - number of rain drops, data_meteoblue.csv - rainfall depth (mm).</p> <p>Milan:</p> <p>Data is provided for 5 rain events in June 2022 at 1 min intervals; data_acoustic_Milan.csv - number of rain drops, data_meteoblue.csv - rainfall depth (mm).</p> <p>The locations of the acoustic sensors and rain gauges can be find in the metadata files: Metadata_acoustic.xlsx and Metadata_meteoblue.xlsx</p> <p>The presented-data passed only a primilinary quality control.</p> <p>Further infromation about the senor networks in Milan and Zurich can be found here: https://doi.org/10.5194/nhess-2022-257</p>
[Data] Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process
<p><br>Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes with multiple materials, addressing challenges in process control and ensuring accurate material composition. Experimental data from processing five powder compositions were collected using a custombuilt monitoring system in a commercial LPBF machine. The research categorised signals from LPBF processing various compositions, enhancing prediction accuracy by combining optical with acoustic data and training convolutional neural networks using contrastive learning. Latent spaces of trained models using two contrastive loss functions, clustered acoustic and optical<br>emissions based on similarities, aligning with five compositions. Contrastive learning and sensor fusion were found to be essential for monitoring LPBF processes involving multiple materials. This research advances the understanding of multi-material LPBF, highlighting sensor fusion strategies’ potential for improving quality control in additive manufacturing. Data set for this work is hosted here</p>
Supporting Movies from: Seismo-acoustic observations of crashing ocean waves: Investigating surf monitoring at Coal Oil Point Reserve, Santa Barbara, California
<div> <div> <div> <p>This repository includes supplementary movies from the manuscript titled, "Seismo-acoustic observations of crashing ocean waves: Investigating surf monitoring at Coal Oil Point Reserve, Santa Barbara, California," submitted to the Journal of Geophysical Research: Solid Earth.</p> <p> </p> <p>Movies S1 and S2. These two movies taken during array deployment 4 on October 20, 2023 show the NW tip of Coal Oil Point at the left of the field of view and Sands Beach northwest of that toward the right. Frames have the same figure layout as Figure 4 of the main text.</p> <p>Movie S3. Same as Movies S1 and S2 but with the NW tip of Coal Oil Point at the right of the field of view and Devereux Beach southeast of that toward the left.</p> </div> </div> </div>
Data from: Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study
<p>This dataset accompanies the following article: "Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study," in <em>IEEE Transactions on Biomedical Engineering</em>, doi: 10.1109/TBME.2023.3263388.</p> <p>Knee acoustic emissions (AE) recorded in the 100-450 kHz and 15-200kHz frequency ranges from a cadaver specimen knee in flexion/extension. Four stages of artificially inflicted cartilage damage and two sensor positions were investigated. </p> <p><em><strong>Stages of artificially inflicted cartilage damage:</strong></em> the cartilage surface damage on the medial compartment, KL III; the cartilage surface damage on the medial compartment plus patellofemoral surface, KL III; the cartilage surface damage on the medial compartment plus on the patellofemoral surface KL IV; the cartilage surface damage on the medial compartment plus on the patellofemoral surface and lateral compartment.</p> <p><strong><em>Sensor positions</em></strong>: medial and lateral knee</p>
NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe
<p>This is the dataset presented in the paper "NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe", which can be found here: <a href="https://arxiv.org/pdf/2412.03633">https://arxiv.org/pdf/2412.03633</a>.</p> <p> </p> <p><strong>Update 2025, June 13th</strong></p> <p>- A <strong>metadata file</strong> can now be found alongside the dataset ("metadata.csv"): it contains the recording date for original NBM files whenever available, and date and location for all but two of the files originating from Xeno-Canto. All audio samples in the train_nbm_orig folder were recorded in France, but no precise location is available for this collection.</p> <p>- Irregularities due to unconstrained text input in several annotation files have been fixed; in particular all annotated parasitic and background noise are now gathered in two classes: <strong>0: Background </strong>and<strong> 0: Other biophonia</strong>. Unidentified bird vocalizations fall under the <strong>Other </strong>category.</p> <p> </p> <p><strong>File description</strong></p> <p>Files come in three directories: train_nbm_orig, train_nbm_xc and test.</p> <p>Each is composed of a list of .wav files with its .txt annotations file. All bird vocalizations are linked to a <strong>species </strong>and are annotated both in <strong>time </strong>and <strong>frequency</strong>.</p> <p>The train dataset is composed of a total of 2,077 audio files and 13,359 annotations, for a total of ~38 hours of recording. Among these, 883 files were downloaded from Xeno-canto and finely hand-annotated.</p> <p> </p> <p><strong>Citation </strong>(Bibtex)</p> <pre>@article{airale2024nbm, title={NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe}, author={Airale, Louis and Pajot, Adrien and Linossier, Juliette}, journal={arXiv preprint arXiv:2412.03633}, year={2024} }<br><br></pre> <p><strong>License</strong></p> <p>This dataset is distributed under a non-commercial, non-derivative license (CC BY-NC-ND 3.0).</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p><span lang="EN-US">Special thanks to all contributors to the initial NBM database: Adrien Pajot, Aymeric Mousseau, Christophe Mercier, Frédéric Cazaban, Gaëtan Mineau, Ghislain Riou, Guillaume Bigayon, Hervé Renaudineau, Kévin Leveque, Lionel Manceau, Mathurin Aubry, Maxence Pajot, Nidal Issa, Willy Raitière, and equal thanks to all anonymous further contributors.</span></p> <p><span lang="EN-US">Acknowledgments also go to all contributors of the Xeno-Canto database whose recordings where used to build this dataset:</span></p> <p><span lang="EN-US">Joost van Bruggen, Ricardo Hevia, Paul Kelly, Franck Hollander, Frédéric Cazaban, Ireneusz Oleksik, Irish Wildlife Sounds, Stanislas Wroza, Cedric Mroczko, Will Scott, Sergi, Chris Batty, Martin Billard, Juha Saari, Julien Bottinelli, Mark Pearson, Uku Paal, Christophe Mercier, Thierry THOMAS, Samuel Levy, Paul Coiffard, Lluís Brotons, Jorge Leitão, Alan Dalton, David Tattersley, Xavier Riera, Lars Mogensen, Feliu López i Gelats, José Manuel Reyes Páez, Jon Sparshott, Sergi Carreras, Marta Celej, julien Rochefort, Chèvremont Fabian, MILLON Xavier, Ed Stubbs, Niels Van Doninck, Jarek Matusiak, Anthony ROUX, Dan Lombard, C PAL, Ray Tsu </span><span lang="EN-US">诸仁</span><span lang="EN-US">, Martijn Verdoes, Pierrick Devoucoux, Manceau Lionel, Lionel Manceau, Vandousselaere Patrick, Volker Arnold, Lars Edenius, Krzysztof Deoniziak, Albert Noorlander, Robert Ekman, Oriol Baena, Mr Mark Shorten, Beatrix Saadi-Varchmin, Simon Kiesé, Robert Manzano, Martin Fousert, Simon Gillings, Lisette, Marco Dragonetti, Cédric PEIGNOT, Adrien CHARBONNEAU, Gosse Hoekstra, Christian Kerihuel, Koen Lepla, Daniele Baroni, Peter van Vlaardingen, Albert Subirà, Alain Malengreau, Julien Piette, Calum Mckellar, Mikael Litsgård, Martin Grienenberger, Sven Kransel, Oliwier Myka, Susanne Kuijpers, Pierre Foulquier, Paolo Zucca, Maties, Geoffrey Monchaux, Jean COURTIN, Pere Josa, Stein Ø. Nilsen, florent yvert, John Sirrett, Toby Carter, Grzegorz Lorek, Tom Jordan, Miguel Tirado, Helder Cardoso, Ignaas Robbe, James P, Corentin Rivière, Romuald Mikusek, David Santamaría Urbano, Diego Fernandez Martinez, Tanguy Loïs, Camille Vacher, Frank Pierik, Testaert Dominique, Piotr Szczypinski, Rafael Costas, Patrick Franke, Ad Hilders, J. Veeken, Thijs Calu, David Pennington, Nic Hallam, Albert Lastukhin, Dominique Guillerme, W. Agster, Nittert van de Water, Marcos Prada Arias, Francesco Barberini, Enrico Hübner, Luca Forneris, Graham Clarke, Mike Douglas, Johan Willner, Sébastien Arriubergé, Rafał Szczerbik, Alessandro Pavesi, Jarred Johnson, Tomasz Wałachowski, Cédric JOUVE, Tom Gheskiere, jesus carrion, David Darrell-Lambert, Graham Sparshott, Théo Hervé, Martin Sutherland, Quentin Giraud, Kévin Giraudin, LE ROY Renaud, August Thomasson, Marcin Sołowiej, Filippo Ceccolini, BirdingAlbufera, Armin Kreusel, Robert Thorpe, Sannier, Oscar Vilches, Paweł Szymański, Jerome Fischer, Matt Slaymaker, Gil Lissens, Sidney M, Matthias Feuersenger, JC Paniagua, Anita Rakitić, david m, Maxime Pirio, brickegickel, Mark Newsome, Sophie REVERDIAU, Oriol, Michaël Bridoux, Sonothèque ADVL, Kieran Nixon, Jochem verweij, Sjouke Scholten, Michael Brunhøj Hansen, Peter Stronach, Pere Josa Anguera, Esperanza Poveda, Johannes Dag Mayer, Pablo Valverde, Friedemann Arndt, Tristan Guillebot de Nerville, Adam Cross, Richard Drew, Cindie Arlaud, Petr Večeřa, Domagoj Tomičić, Guillaume Petitjean, riou, Peter Mattsson, Michał Jezierski, SCECB NATURA-Z, Karol Łanocha, Twan Mols, Klaus Fink, Dawid Jablonski, Arnold Meijer, Paul Ehlers, José Carlos Sires, manuel Grosselet, Birding The Strait, Charlie Bodin, Juan Carlos Paniagua, Nabholz Benoit, Fergus Crystal, Agris Celmins, Albert Cama, Iván Vega, John Muddeman, Adrien Mauss, Gerard Troost, Serge Hoste, Stefano Miceli, Joan Balfagon, Ace, Frank A. Roos, Tom Wulf, Vincent Palomares, Marcel Tenhaeff, Gabriel Hasan, James Lidster, Frank van de Weijer, Herman van Oosten, Alain Verneau, Herman van der Meer, Romain SPELLER, Johan Lorentzon, Thomas ARMAND, Chris Beach, Jacob Spinks, András Schmidt, Jonathan van Erkel, James Spencer, Rowan Wakefield, LEPAGE Frédéric, Itziar Gutiérrez, Severin Racky, NEVILLE MADON, B Whyte, Bodhuin Maxime, Benoit Paepegaey, Ruysschaert, Soulier Pierrick, Frederik Fluyt, Thomas Roux, Eduardo Realinho, Simon Elliott, Oscar Vilches Mendoza, Juan Pita-Romero Caamaño, Gary Elton, Marc Hughes, Raul Pascual, Nicolas Selosse, Arnaud Hedel, Gaëtan Mineau, Peter Boesman, Steve Flynn, Gerry p oneill, Andy Hultberg, Helmut Schaffer, Luca Giussani, edouard dansette, Gavia Stellata, Robert Schouw, Sławomir Karpicki-Ignatowski, mvallespirc, Mark Plummer, Chacron, Seynaeve Adriaan, Kristian Whittaker, Andreas Pettersson, Rafał Kurowski, Delpit, Anja van Halbeek, Lars Burnus, Benjamin Schedl, Sreekumar Chirukandoth, Mathias Götz, Mikhail Velikanov, Paul Bourdin, Török Tamás, Mehmet Ali Demiral, Manuel Grosselet, Olivier Swift, Yoann Blanchon, Jacob Bosma, Alexis Bukowski, Götz Ellwanger, Guillaume Bigayon, Charlie BODIN, Olivier SWIFT, Manuel Grosselet, Tero Linjama, Ad Postma, Eetu Paljakka, Jan Hein van Steenis, Alwin van Lubeck, Jacobo Ramil MIllarengo, Andrew Cobley, guus van duin, Meinolf Ottensmann, Steve Thorpe, Antoine Salmon, Alain Beuget, Simon Busuttil, Bertrand Dallet, Bill Haines, Robbin van Dijk, Jacopo Barchiesi, Johan Jordaans, Simon BAUDOUIN, Nelson Conceição, Teet Sirotkin, Dean McDonnell, Jocce Ekstrom, Michael John O Mahony, Fred Prak, Joachim Pintens, Christophe Legrand, Daniel Beuker, G Berger, Steve Blain, Boris Delahaie, Will Langdon, David Melichar, Emmanuel Roy, Jonas Brüggeshemke, YvesDS, FRIEDRICH Richard</span></p>
Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal Passive Acoustic Monitoring datasets
<p>Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches, and indices are best suited for characterizing marine and terrestrial acoustic environments.</p> <p>Here, we describe the application of multiple machine-learning techniques to the analysis of a large PAM dataset. We combine pre-trained acoustic classification models (VGGish, NOAA & Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine environment.</p> <p>The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labelled sounds in the 8 kHz range, however, low and high-frequency sounds could not be classified using this approach.</p> <p>The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow for establishing a link between ecologically relevant information and PAM recordings at multiple scales.</p> <p>The datasets and scripts provided in this repository allow replicating the results presented in the publication. </p>
Nocturnal flight calls dataset: long-term acoustic monitoring of birds migrating at night
<p><strong>General Description:</strong></p> <p>This is a development set used in the experiments in the Ph.D. thesis: "Nowe metody akustycznej identyfikacji ptaków migrujących nocą" (<em>"Novel methods of acoustic identification of birds migrating at night"</em>) by Hanna Pamula. The project focuses on the detection (and - partially - classification) of passerine birds' calls from long-term audio recordings collected during bird autumn migration between 2016 and 2019. The dataset consists of >56,5 hours of recordings with annotations of nocturnal flight calls of passerine birds migrating along the Baltic Sea coast, Poland.</p> <p> </p> <p><strong>Folder Structure</strong></p> <p>Development_Set_3.1.zip</p> <p>|_Development_Set_3.1/</p> <p> |__Training_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p> |__Validation_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p> |__Testing_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p>Training Set: 86 recordings</p> <p>Validation Set: 8 recordings</p> <p>Testing set: 18 recordings (BUT: uploaded 20 recordings, as in the previous version of the dataset - version 3, two additional recordings were used. Then, they were deleted in the final version of development set 3.1. Two additional recordings are: 'BUK5_20161101_002104a and BUK5_20161101_002104b)</p> <p>Names of waveforms and annotations are matching.</p> <p><strong>Waveforms:</strong></p> <p>The whole dataset consists of 114 recordings. One hundred thirteen recordings are about 30 minutes long (29min56s – 29min 59s), one recording is 1min20s. All data were recorded at 44,100 Hz sampling rate, one channel, with SM2 Wildlife Acoustics recorders + SMX-NFC microphone. The recording sessions were performed at night (starting time and date denoted in a file name) on the Baltic Sea coast in Poland (Dąbkowice, near Darłowo).</p> <p><strong>Annotations:</strong></p> <p>Transcriptions were produced using Audacity 2.4.1: https://www.audacityteam.org/ by an experienced birdwatcher, Hanna Pamula. While every effort has been made to ensure the quality and accuracy of the labels, some errors may occur, taking into account the difficulty of nocturnal call recognition and transcription tasks in general.</p> <p>Transcription format:</p> <p>[Starting time (sec)] [Ending time (sec)] [Label]</p> <p><strong>Meaning of the labels:</strong></p> <p>1. Positive classes – migrating passerine birds:</p> <ul> <li>'s' – song thrush call (Turdus philomelos)</li> <li>'k' – blackbird call (Turdus merula)</li> <li>'d' – redwing call (Turdus iliacus)</li> <li>'r' – robin call (Erithacus rubecula)</li> <li>‘kwiczol’ – fieldfare call (Turdus pilaris)</li> <li>‘skowronek’ – skylark call (Alauda arvensis)</li> <li>Each of the above labels could also have a question mark '?', e.g. 'r?', 'k?' – meaning that it's not a sure label. In a bird call detection task, they are regarded as positive chunks containing bird call(s).</li> <li>'ni' – non identified bird call (distant/quiet/not recognized)</li> </ul> <p>Only the supposed calls of migrating passerine birds were labeled; other sounds of species were ignored (e.g., robin's tik-calling, which can be often heard at dusk, and may be regarded as warning sounds).</p> <p>2. Negative classes – other marked sound events:</p> <ul> <li>'g' – other bird calls/songs/sounds. Sounds that could confuse the model; for example, sounds of migrating geese, cranes, plovers calls, etc.</li> <li>'gh' – human voices</li> <li>'t' – cracks, clicks, raindrops, other noise</li> <li>‘puszczyk’ – tawny owl voice (Strix aluco)</li> <li>'czapla' – grey heron voice (Ardea cinerea)</li> </ul> <p>Not all occurrences of the negative sounds were labeled – only some chosen examples to represent the possible noises/negative samples. Thus these annotations can't be used for entirely different detection / classification tasks than intended, e.g., detecting migrating cranes or human voices in long-term recordings.</p> <p>3. Labels to be excluded from analysis:</p> <ul> <li>'???', '??? mysz', '??? high freq' – unknown, not sure if the sound event is a birds' call or not. Uncertainty about belonging to a positive/negative class in the detection task.</li> </ul>
Estimating the abundance of the critically endangered Baltic Proper harbour porpoise (Phocoena phocoena) population using passive acoustic monitoring
<p>Knowing the abundance of a population is a crucial component to assess its conservation status and develop effective conservation plans. For most cetaceans, abundance estimation is difficult given their cryptic and mobile nature, especially when the population is small and has a transnational distribution. In the Baltic Sea, the number of harbour porpoises (<i>Phocoena phocoena</i>) has collapsed since the mid-20<sup>th</sup> century and the Baltic Proper harbour porpoise is listed as Critically Endangered by the IUCN and HELCOM; however, its abundance remains unknown. Here, one of the largest ever passive acoustic monitoring studies was carried out by eight Baltic Sea nations to estimate the abundance of the Baltic Proper harbour porpoise for the first time. By logging porpoise echolocation signals at 298 stations during May 2011-April 2013, calibrating the loggers' spatial detection performance at sea, and measuring the click rate of tagged individuals, we estimated an abundance of 71-1,105 individuals (95% CI, point estimate 491) during May-October within the population's proposed management border. The small abundance estimate strongly supports that the Baltic Proper harbour porpoise is facing an extremely high risk of extinction, and highlights the need for immediate and efficient conservation actions through international cooperation. It also provides a starting point in monitoring the trend of the population abundance to evaluate the effectiveness of management measures and determine its interactions with the larger neighbouring Belt Sea population. Further, we offer evidence that design-based passive acoustic monitoring can generate reliable estimates of the abundance of rare and cryptic animal populations across large spatial scales.</p>
Recordings from: Evaluation of a coastal acoustic buoy for cetacean detections, bearing accuracy, and exclusion zone monitoring
<p>1.<span> </span>There is strong socio-political support for offshore wind development in US territorial waters, and construction is planned off several east coast states. Some of the planned development sites coincide with important habitat for critically endangered North Atlantic right whales. Both exclusion zones and passive acoustic monitoring are important tools for managing interactions between marine mammals and human activities. Understanding where animals are with respect to exclusion zones is important to avoid costly construction delays while minimizing the potential for negative impacts. Impact piling from construction of hundreds of offshore wind turbines likely requires exclusion zones as large as 10 km.</p> <p>2.<span> </span>We have developed a three-hydrophone passive acoustic monitoring system that provides bearing information along with marine mammal detections to allow for informed management decisions in real-time. Multiple units form a monitoring system designed to determine whether marine mammal calls originate from inside or outside of an exclusion zone. In October 2021 we undertook a full system validation, with a focus on evaluating the detection range and bearing accuracy of the system with respect to right whale upcalls. Five units were deployed in Mid-Atlantic waters and we played more than >3,500 simulated right whale upcalls at known locations to characterize the detection function and bearing accuracy of each unit. The modeled results of the detection function error were then used to compare the effectiveness of a bearing-based system to a single sensor that can only detect a signal but not ascertain directivity.</p> <p>3.<span> </span>Field trials indicated maximum detection ranges from 4–7.3 km depending on source and ambient noise levels. Simulations showed that incorporating bearing detections provides a substantial improvement in false alarm rates (6 to 12 times depending on number of units, placement, and signal to noise conditions) for a small increase in the risk of missed detections inside of an exclusion zone (1–3%). </p> <p>4.<span> </span>We show that the system can be used for monitoring exclusion zones and clearly highlight the value of including bearing estimation into exclusion zone monitoring plans while noting that placement and configuration of units should reflect anticipated ambient noise conditions.</p>
Unlabeled AnuraSet: A dataset for leveraging unlabeled data in machine learning models for passive acoustic monitoring
<p>The Unlabeled AnuraSet (U-AnuraSet) is an extension of the original AnuraSet dataset. It consists of soundscape recordings from passive acoustic monitoring conducted in Brazil. The recording sites are identical to those in the original AnuraSet. Each site comprises 2,666 one-minute raw audio files of unlabeled data. The U-AnuraSet is publicly available to encourage machine learning researchers to explore innovative methods for leveraging unlabeled data in the training of models aimed at solving problems such as anuran call identification.</p> <p>If you find the Unlabeled AnuraSet useful for your research, please consider citing it as follows:</p> <p>Cañas, J.S., Toro-Gómez, M.P., Sugai, L.S.M., et al. A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring. Sci Data 10, 771 (2023). https://doi.org/10.1038/s41597-023-02666-2</p>
Figure 3 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U.S.A.
Figure 3. Percent of porpoise-positive minutes (PPM) that contained at least five click trains with minimum interclick intervals (MICIs) of <10 ms, thus classified as a buzz-positive minute (BPM). The star symbols and brackets represent post hoc Tukey tests that gave significant results at the P <0.05 level: Morning ťs. Day and Day ťs. Night for the offshore site.
Figure 4 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U.S.A.
Figure 4. Distribution of harbor porpoise acoustic activity at the reef site measured as (a) porpoise positive minute (PPM) and (b) buzz positive minute (BPM) as a function of the tidal cycle. The length of the bars represents the binned presence of PPM or BPM during a given tidal phase. The black arrows represent the peak in mean PPMs and BPMs, respectively.
Figure 2 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U.S.A.
Figure 2. Percent of daily monitored minutes in which harbor porpoise were detected for the reef and offshore sites throughout the study period. The gray shaded areas represent data gaps between deployments.
Figure 1 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U.S.A.
Figure 1. Bathymetric overview of study area in coastal Oregon (see inset) with acoustic instrumentation deployment sites displayed by the black dots.
Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable"
<p>Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable" </p> <p><a href="../api/records/13133835/draft/files/tmdcm.txt/content" target="_blank" rel="noopener noreferrer">tmdcm.txt</a>: current meter data </p> <p><a href="../api/records/13133835/draft/files/tide.txt/content" target="_blank" rel="noopener noreferrer">tide.txt</a>: tidal gauge data </p> <p><a href="../api/records/13133835/draft/files/windspeed.txt/content" target="_blank" rel="noopener noreferrer">windspeed.txt</a>: windspeed data </p> <p>Figure 2: Figure2.npy</p> <p>Figure 3: Figure 3 abc .npy</p> <p>Figure16: <a href="../api/records/13133835/draft/files/spatial_Vc.npy/content" target="_blank" rel="noopener noreferrer">spatial_Vc.npy</a> & <a href="13133835" target="_blank" rel="noopener noreferrer">spatial_h.npy</a> </p> <p>Figure 17: <a href="../api/records/13133835/draft/files/streching_ncf.npy/content" target="_blank" rel="noopener noreferrer">streching_ncf.npy</a></p>
DCASE 2018, Task 5: Monitoring of domestic activities based on multi-channel acoustics - Development dataset
<p>This repository contains the development data of task 5 of the DCASE 2018 challenge. The dataset is a derivative of the SINS database.</p> <p>The SINS database contains a continuous recording of one person living in a vacation home over a period of one week. The recordings were manually annotated on daily activity level: "Cooking", "Dishwashing", "Eating", "Social activity (visit, phone call)", "Vacuum cleaning", "Watching TV", "Working", "Presence" and "Absence". More information can be found on (please cite this papers when using the dataset):</p> <p>G. Dekkers, S. Lauwereins, B. Thoen, M. W. Adhana, H. Brouckxon, T. van Waterschoot, B. Vanrumste, M. Verhelst, and P. Karsmakers, “The SINS database for detection of daily activities in a home environment using an acoustic<br> sensor network,” in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), Munich, Germany, November 2017, pp. 32–36.</p> <p>G. Dekkers, L. Vuegen, T. van Waterschoot, B. Vanrumste, and P. Karsmakers, “DCASE 2018 Challenge - Task 5: Monitoring of domestic activities based on multi-channel acoustics,” KU Leuven, Tech. Rep., July 2018.</p> <p>The derivative of the SINS database, 'DCASE 2018 – Task 5 development dataset' consists of data collected by 4 microphone arrays in the combined living room and kitchen area. The continuous recordings were split into audio segments of 10s. These audio segments are provided as individual files along with the ground truth. In total 72984 segments are made available, leading to approximately 200 hours of data.</p> <p>More information about the challenge and the specific dataset can be found <a href="http://dcase.community/challenge2018/task-monitoring-domestic-activities">here</a>. Information solely related to the content of the dataset is available in 'DCASE2018-task5-dev.doc.zip'. <br> <br> <strong>By accessing or using this database, the user accepts the provided EULA (available in DCASE2018-task5-dev.doc.zip).</strong></p>
A river on fiber: high resolution fluvial monitoring with distributed acoustic sensing – Data, Matlab Scripts and App
<p>Matlab software and data associated with Roth et al. (submitted to Seismica, 2025).</p>
DCASE 2018, Task 5: Monitoring of domestic activities based on multi-channel acoustics - Evaluation dataset
<p>The dataset is a derivative of the SINS dataset and is meant to be used as an evaluation set for the <a href="http://dcase.community/challenge2018/task-monitoring-domestic-activities">DCASE2018 Task 5 challenge</a>. The development set to be used can be found <a href="https://zenodo.org/record/1247102#.WzIF_NUzZhE">here</a>. The dataset is a derivative of the SINS database.</p> <p>The SINS database contains a continuous recording of one person living in a vacation home over a period of one week. The recordings were manually annotated on daily activity level: "Cooking", "Dishwashing", "Eating", "Social activity (visit, phone call)", "Vacuum cleaning", "Watching TV", "Working", "Presence" and "Absence". More information can be found on (please cite this papers when using the dataset):</p> <p>G. Dekkers, S. Lauwereins, B. Thoen, M. W. Adhana, H. Brouckxon, T. van Waterschoot, B. Vanrumste, M. Verhelst, and P. Karsmakers, “The SINS database for detection of daily activities in a home environment using an acoustic<br> sensor network,” in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), Munich, Germany, November 2017, pp. 32–36.</p> <p>G. Dekkers, L. Vuegen, T. van Waterschoot, B. Vanrumste, and P. Karsmakers, “DCASE 2018 Challenge - Task 5: Monitoring of domestic activities based on multi-channel acoustics,” KU Leuven, Tech. Rep., July 2018.</p> <p>The derivative of the SINS database, 'DCASE 2018 – Task 5 evaluation dataset' consists of data collected by 7 microphone arrays in the combined living room and kitchen area. The continuous recordings were split into audio segments of 10s. These audio segments are provided as individual files. In total 72972 segments are made available, leading to approximately 200 hours of data with annotations.</p> <p>More information about the challenge and the specific dataset can be found here. Information solely related to the content of the dataset is available in 'DCASE2018-task5-eval.doc.zip'.</p> <p>By accessing or using this database, the user accepts the provided EULA (available in DCASE2018-task5-eval.doc.zip).</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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