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130 results for “acoustic monitoring”
Table 1 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U. S. A.
<p><i>Table 1.</i> Details of digital acoustic monitoring (DMON) deployment sites and recording times over the duration of the study.</p><table><tbody><tr><th>Site</th><th>Coordinates</th><th>Deployment date</th><th>Recovery date</th><th>Deployment duration (time)</th><th>Recorded minutes</th></tr></tbody><tbody><tr><th>Reef Offshore</th><td>44 Ǫ 35.140 N, 124 Ǫ 07.120 W 44 Ǫ 35.121 N, 124 Ǫ 07.000 W 44 Ǫ 34.985 N, 124 Ǫ 07.117 W 44 Ǫ 35.247 N, 124 Ǫ 06.815 W 44 Ǫ 35.221 N, 124 Ǫ 06.859 W 44 Ǫ 34.650 N, 124 Ǫ 13.218 W 44 Ǫ 34.857 N, 124 Ǫ 13.419 W 44 Ǫ 34.929 N, 124 Ǫ 13.215 W 44 Ǫ 34.929 N, 124 Ǫ 13.215 W 44 Ǫ 34.929 N, 124 Ǫ 13.215 W</td><td>16 May 2014 12 Jun 2014 26 Jun 2014 29 Jul 2014 16 Sep 2014 12 Jun 2014 26 Jun 2014 29 Jul 2014 16 Sep 2014 20 Sep 2014</td><td>23 May 2014 20 Jun 2014 7 Jul 2014 8 Aug 2014 18 Sep 2014 20 Jun 2014 7 Jul 2014 8 Aug 2014 29 Sep 2014 13 Oct 2014</td><td>6 d 20 h 00 min 7 d 21 h 30 min 10 d 14 h 40 min 9 d 21 h 40 min 2 d 13 h 20 min 7 d 21 h 40 min 10 d 23 h 10 min 10 d 0 h 30 min 12 d 17 h 40 min 12 d 12 h 00 min</td><td>985 1,138 1,529 1,427 441 1,138 1,580 1,444 1,835 1,801</td></tr></tbody></table>
Table 4 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U. S. A.
<p><i>Table 4.</i> Predictor environmental variables in generalized additive models (GAM) of harbor porpoise echolocation activity and their significance, with deviance explained of entire model.</p><table><tbody><tr><th></th><th>Reef</th><th>Reef</th><th>Offshore</th><th>Offshore</th></tr></tbody><tbody><tr><th>Predictor</th><td>PPM</td><td>BPM</td><td>PPM</td><td>BPM</td></tr><tr><th>Julian day</th><td><0.001c</td><td><0.001c</td><td><0.001c</td><td><0.001c</td></tr><tr><th>Diel phase:</th><td></td><td></td><td></td><td></td></tr><tr><th>Morning</th><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><th>Day Evening Night</th><td>— — 0.01a</td><td>— — —</td><td>— 0.003b 0.04a</td><td>— <0.001c</td></tr><tr><th>Tidal phase</th><td>0.05</td><td>—</td><td>0.05</td><td>—</td></tr><tr><th>Julian day <i>×</i> Diel</th><td></td><td></td><td></td><td></td></tr><tr><th>phase: Morning Day Evening Night Julian day <i>×</i> Tidal</th><td>— — <0.001c <0.001c 0.02a</td><td>— 0.04a 0.001b <0.001c <0.001c</td><td>— <0.001c <0.001c <0.001c —</td><td><0.001c <0.001c — —</td></tr><tr><th>phase</th><td></td><td></td><td></td><td></td></tr><tr><th>Diel phase <i>×</i> Tidal</th><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><th>phase</th><td></td><td></td><td></td><td></td></tr><tr><th>Deviance explained</th><td>6.9%</td><td>13.7%</td><td>11.5%</td><td>13.2%</td></tr></tbody></table><p><sup>a</sup> Significant at the 0.05 probability level.</p><p><sup>b</sup> Significant at the 0.01 probability level.</p><p><sup>c</sup> Significant at the 0.001 probability level.</p>
Table 3 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U. S. A.
<p><i>Table 3.</i> Simultaneous DMON site deployment data: For the periods when recording devices were present at both the reef and offshore sites and simultaneously recording, the number and percent of porpoise positive minutes (PPMs), and buzz positive minutes (BPMs) detected at each site are given, as well as the number and percent (displayed in bold) that were detected at both sites simultaneously during the same 10 min time period.</p><table><tbody><tr><th>Detection type</th><th>Location</th><th>Number of detections</th><th>Percent of total detection type</th></tr></tbody><tbody><tr><th>PPMs</th><td>Reef Offshore Present at both</td><td>1,297 514 521</td><td>55.6% 22.0% <b>22.3%</b></td></tr><tr><th>BPMs</th><td>Reef Offshore Present at both</td><td>546 207 43</td><td>68.6% 26.0% 5.4%</td></tr></tbody></table>
Table 2 in Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U. S. A.
<p><i>Table 2.</i> Recorded data set for the entire time of investigation, separated for each station by diel phase and their totals. PPM: porpoise positive minute; BPM: buzz-positive minute.</p><table><tbody><tr><th></th><th>Diel</th><th>Recorded</th><th>Porpoise positive</th><th>PPM (% per complete</th><th>Click only</th><th>Buzz positive</th><th>BPM (%</th></tr></tbody><tbody><tr><th>Site</th><td>phase</td><td>minutes</td><td>minutes (PPM)</td><td>observation period)</td><td>minutes</td><td>minutes (BPM)</td><td>per PPM)</td></tr><tr><th>Reef</th><td>Morning</td><td>278</td><td>92</td><td>33.1</td><td>66</td><td>26</td><td>28.3</td></tr><tr><th></th><td>Day</td><td>3,163</td><td>1,239</td><td>39.2</td><td>876</td><td>363</td><td>29.3</td></tr><tr><th></th><td>Evening</td><td>286</td><td>104</td><td>36.4</td><td>68</td><td>36</td><td>34.6</td></tr><tr><th></th><td>Night</td><td>1,793</td><td>622</td><td>34.7</td><td>436</td><td>186</td><td>29.9</td></tr><tr><th>Offshore</th><td>Morning</td><td>353</td><td>62</td><td>17.6</td><td>40</td><td>22</td><td>35.5</td></tr><tr><th></th><td>Day</td><td>4,075</td><td>730</td><td>17.9</td><td>593</td><td>137</td><td>18.8</td></tr><tr><th></th><td>Evening</td><td>351</td><td>78</td><td>22.2</td><td>64</td><td>14</td><td>17.9</td></tr><tr><th></th><td>Night</td><td>3,019</td><td>550</td><td>18.2</td><td>370</td><td>180</td><td>32.7</td></tr></tbody></table>
Pin-tailed whydah and Cape robin-chat calls for passive acoustic monitoring
<p>We provide the audio data (.wav) used to train neural network classifiers along with the corresponding labelled files (.svl). The .svl files are natively read using Sonic Visualiser (https://www.sonicvisualiser.org/) but can directly be read using Python as these are XML files.</p> <p>This is a three class classification dataset. The recordings were obtained using an AudioMoth which was placed at one location in Intaka Island Nature Reserve, Cape Town, South Africa. The recorder was attached to a tree at approximately 1.5 meters from the ground. The sampling rate was set to 48000Hz with a bit rate of 768kbps. The recordings took place in January 2021. While further recordings exist we only provide a small subset here. Additional data can be requested.</p> <p><strong>Files provided</strong></p> <p>Audio.zip - contains audio files (.wav)</p> <p>Annotations.zip - contains the corresponding labels (.svl) for Sonic Visualiser</p> <p><strong>Class description</strong></p> <p>CRC: calls of the Cape robin-chat (Cossypha caffra)</p> <p>PTW: calls of the pin-tailed whydah (Vidua macroura)</p> <p>NOISE: any sound event that does not contain a Cape robin-chat or pin-tailed whydah call</p> <p> </p> <p>Parts of this data was used in two MSc dissertations:</p> <ul> <li>"Acoustic Data Augmentation for Small Passive Acoustic Monitoring Datasets", Aime Nshimiyimana, African Centre of Excellence in Data Science (ACE-DS) of the University of Rwanda, College of Business and Economics</li> <li>"Pre-training neural networks on Xeno-Canto and eBird for bioacoustic classification models", Mikwa Boris Tamanjong, African Centre of Excellence in Data Science (ACE-DS) of the University of Rwanda, College of Business and Economics</li> </ul>
Audio files and dataset: Guess who? Evaluating individual acoustic monitoring for males and females of the Tawny Pipit, a migratory passerine bird with a simple song
<p>We have uploaded the audio files (song files + recording files) and the dataset of the analysis conducted in our manuscript entitled ''Guess who? Evaluating individual acoustic monitoring for males and females of the<br> Tawny Pipit, a migratory passerine bird with a simple song''. This study is published in the scientific journal ''Journal of Ornithology''. We have also provided a txt. file, called 'Read me' explaining the uploaded data in more detail.</p>
Passive acoustic monitoring indicates Barred Owls are established in northern coastal California and management intervention is warranted
<p>Barred Owls (<em>Strix varia</em>) have recently expanded westward from eastern North America, contributing to substantial declines in Northern Spotted Owls (<em>Strix occidentalis caurina</em>). Passive acoustic monitoring (PAM) represents a potentially powerful approach for tracking range expansions like the Barred Owl's, but further methods development is needed to ensure that PAM-informed occupancy models meaningfully reflect population processes. Focusing on the leading edge of the Barred Owl range expansion in coastal California, we used a combination of PAM data, GPS-tagging, and active surveys to (1) estimate breeding home range size, (2) identify patterns of vocal activity that reflect resident occupancy, and (3) estimate resident occupancy rates. Mean breeding season home range size (452 ha) was reasonably consistent with the size of cells (400 ha) sampled with autonomous recording units (ARUs). Nevertheless, false-positive acoustic detections of Barred Owls frequently occurred within cells not containing an activity center such that site occupancy estimates derived using all detected vocalizations (0.61) were unlikely to be representative of resident occupancy. However, the proportion of survey nights with confirmed vocalizations (VN) and the number of ARUs within a sampling cell with confirmed vocalizations (VU) were indicative of Barred Owl residency. Moreover, the false positive error rate could be reduced for occupancy analyses by establishing thresholds of VN and VU to define detections, although doing so increased false negative error rates in some cases. Using different thresholds of VN and VU, we estimated resident occupancy to be 0.29–0.44, which indicates that Barred Owls have become established in the region but also that timely lethal removals could still help prevent the extirpation of Northern Spotted Owls. Our findings provide a scalable framework for monitoring Barred Owl populations throughout their expanded range and, more broadly, a basis for converting site occupancy to resident occupancy in PAM programs. </p>
Open‐source workflow approaches to passive acoustic monitoring of bats
<ol> <li>The affordability, storage, and power capacity of compact modern recording hardware has evolved passive acoustic monitoring (PAM) of animals and soundscapes into a non-invasive, cost-effective tool for research and ecological management and is particularly effective for bats and toothed whales that consistently echolocate. The use of PAM at large scales hinges on effective automated detectors and species classifiers which, combined with distance sampling approaches, have enabled species abundance estimation of toothed whales. But standardized, user-friendly, and open-access automated detection and classification workflows are in demand for this key conservation metric to be realized for bats.</li> <li>We used the PAMGuard toolbox including its new deep learning classification module to test the performance of four open-source workflows for automated analyses of acoustic datasets from bats. Each workflow used a different initial detection algorithm followed by the same deep learning classification algorithm and was evaluated against the performance of an expert manual analyst.</li> <li>Workflow performance depended strongly on the signal-to-noise ratio and detection algorithm used: the full deep learning workflow had the best classification accuracy (≤67%) but was computationally too slow for practical large-scale bat PAM. Workflows using PAMGuard's detection module or triggers onboard an SM4BAT or AudioMoth accurately classified up to 47%, 59% and 34%, respectively, of calls to species. Not all workflows included noise sampling critical to estimating changes in detection probability over time, a vital parameter for abundance estimation. The workflow using PAMGuard's detection module was 40 times faster than the full deep learning workflow and missed as few calls (recall for both ~0.6), thus balancing computational speed and performance. </li> <li>We show that complete acoustic detection and classification workflows for bat PAM data can be efficiently automated using open-source software such as PAMGuard and exemplify how detection choices, whether pre- or post-deployment, hardware or software-driven, affect the performance of deep learning classification and <span>the downstream ecological information that can be extracted from acoustic recordings. In particular, understanding, and quantifying detection/classification accuracy and the probability of detection are key to avoid introducing biases that may ultimately affect the quality of data for ecological management. </span> </li> </ol>
Dataset for "Benchmarking for the automated detection of southern yellow-cheeked crested gibbon calls from passive acoustic monitoring data"
<p>"<span>Benchmarking automated detection and classification approaches for long-term acoustic monitoring of endangered species: a case study on gibbons from Cambodia</span>"</p> <div> <p><span>Recent advances in deep learning and transfer learning have revolutionized our ability for the automated detection of acoustic signals from long-term soundscape recordings. Here, we provide a benchmark for the automated detection of southern yellow-cheeked crested gibbon (<em>Nomascus gabriellae</em>) calls recorded in Jahoo, Cambodia. For the benchmarking, we compared the performance of support vector machines (SVMs), a quasi-DenseNet architecture (Koogu), transfer learning with ResNet50 models trained on the ‘ImageNet’ dataset (ResNet), and transfer learning with embeddings from a global birdsong model (BirdNET). We also investigated the impact of varying the number of training samples on the performance of these models. Transfer learning models based on <span>BirdNET embeddings had superior performance with a smaller number of training samples, whereas Koogu and ResNet models only had acceptable performance with a larger number of training samples (>200 gibbon samples). We deployed the BirdNET-based model over </span>> 130,000 hours<span> of continuous soundscape data, which, after manual review, resulted in >12,000 verified true positive detections. We found that gibbon calling events occurred mostly in the early morning hours between 05:00 to 0:600 local time. We had fewer gibbon detections during the monsoon period and found substantial variation in spatial patterns of calling events across months and years. </span>We show that automated detection can be used to investigate long-term spatial and temporal patterns of gibbon calling events. Reliable automated detection approaches are a critical first step for using passive acoustic monitoring to assess endangered gibbon populations at ecologically relevant temporal- and spatial-scales. </span></p> <p> Detailed instructions regarding use are provided on GitHub.</p> </div> <p>Link to GitHub: https://github.com/DenaJGibbon/benchmark-gibbon-calls.</p> <p>Please cite both if you use these data: </p> <p>Clink, D., Cross-Jaya, H., Kim, J., Ahmad, A. H., Hong, M., Sala, R., Birot, H., Agger, C., Vu, T. T., Thi, H. N., Chi, T. N., & Klinck, H. (2024). Dataset for "Benchmarking for the automated detection of southern yellow-cheeked crested gibbon calls from passive acoustic monitoring data" [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12706803" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12706803</a></p> <p>Clink DJ, Cross-Jaya H, Kim J, Ahmad AH, Hong M, Sala R, Birot H, Agger C, Vu TT, Thi HN, Chi TN. Benchmarking for the automated detection and classification of southern yellow-cheeked crested gibbon calls from passive acoustic monitoring data. bioRxiv. 2024:2024-08.</p>
Evaluation of Respiratory Acoustic Monitor in Children After Surgery
ClinicalTrials.gov study NCT02256384. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Contributions of environmental conditions and sound characteristics to differences in perceptibility: Recommendations for passive acoustic monitoring
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Open‐source workflow approaches to passive acoustic monitoring of bats
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Evaluating the potential effects of capturing and handling on subsequent observations of a migratory passerine through individual acoustic monitoring
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Data from: Passive acoustic monitoring provides reliable under-estimates of population size and longevity in wild Savannah Sparrows
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Statistical code from: Passive acoustic monitoring with AI-based detection and identification reveal sooty grouse hooting patterns in western Oregon
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Data and code for: Acoustic monitoring enables multi-taxa conservation assessment and prioritisation over large scales and for rare and cryptic species
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Passive acoustic monitoring indicates Barred Owls are established in northern coastal California and management intervention is warranted
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Data from: Early detection of human impacts using acoustic monitoring: an example with forest elephants
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Estimating spatio-temporal reproductive dynamics of fish populations with passive acoustic monitoring: A state-space model approach
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Examples of killer whale (Orcinus orca) calls from passive acoustic monitoring in the Gulf of Alaska
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