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59 results for “Passive acoustics”
Black-and-white ruffed lemur (Varecia variegata) calls for passive acoustic monitoring
<p>Data accompanying the paper: "Passive Acoustic Monitoring and Transfer Learning"</p> <p><strong>Please cite this dataset as:</strong></p> <blockquote> <p>Dufourq, Emmanuel and Batist, Carly and Foquet, Ruben and Durbach, Ian. (2022). Passive Acoustic Monitoring and Transfer Learning. BioRxiv doi: </p> </blockquote> <p>This dataset contains approximately 60 hours of audio that contained calls of the critically endangered Black-and-white ruffed lemur (Varecia variegata). The audio data was collected in a sub-humid rainforest site (Mangevo) in the southeast of Ranomafana National Park in Madagascar using 2 Swift recorders (Cornell Center for Conservation Bioacoustics). The sampling rate was set to 48,000Hz and the recordings were collected intermittently between May 2019 and November 2020. A larger dataset exists and further recordings will be released.</p> <p>The annotations files are in (.svl) format which is compatible with SonicVisualiser (https://www.sonicvisualiser.org/). Each audio file has a corresponding .svl file. Each .svl has segments of audio that were manually annotated as either ''thyolo-alethe" (presence class) or "noise" (absence class) -- this dataset can be used to train a binary classification model.</p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip".</p>
Hainan gibbons (Nomascus hainanus) calls for passive acoustic monitoring
<p><em>This dataset extends an existing one (10.5281/zenodo.3991714).</em></p> <p>Data accompanying the paper: "Passive Acoustic Monitoring and Transfer Learning"</p> <p><strong>Please cite this dataset as:</strong></p> <blockquote> <p>Dufourq, Emmanuel and Batist, Carly and Foquet, Ruben and Durbach, Ian. (2022). Passive Acoustic Monitoring and Transfer Learning. BioRxiv doi: </p> </blockquote> <p>This dataset contains approximately 10 hours of audio that contained calls of the critically endangered Hainan gibbons (Nomascus hainanus). The audio data was collected in the Bawangling National Nature Reserve, Malawi using 8 Song Meter SM3 recorders. The sampling rate was set to 9,600Hz and the recordings were collected between March to August 2016.</p> <p>The annotations files are in (.svl) format which is compatible with SonicVisualiser (https://www.sonicvisualiser.org/). Each audio file has a corresponding .svl file. Each .svl has segments of audio that were manually annotated as either ''gibbon" (presence class) or "no-gibbon" (absence class) -- this dataset can be used to train a binary classification model.</p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip".</p>
Data from: Passive acoustic monitoring provides reliable under-estimates of population size and longevity in wild Savannah Sparrows
<p>Many breeding birds produce conspicuous sounds, providing tremendous opportunities to study free-living birds through acoustic recordings. Traditional methods for studying population size and demographic features depend on labour-intensive field research. Passive acoustic monitoring provides an alternative method for quantifying population size and demographic parameters, but this approach requires careful validation. To determine the accuracy of passive acoustic monitoring for estimating population size and demographic parameters, we used autonomous recorders to sample an island-living population of Savannah Sparrows (<em>Passerculus sandwichensis</em>) over a six-year period. Using the individually distinctive songs of males, we estimated male population size as the number of unique songs detected in the recordings. We analyzed songs across six years to estimate birth year, death year, and longevity. We then compared the estimates to field data in a blind analysis. Estimates of male population size through passive acoustic monitoring were, on average, 72% of the true male population size, with higher accuracy in lower-density years. Estimates of demographic rates were lower than true values by 29% for birth year, 23% for death year, and 29% for longevity. This is the first investigation to estimate longevity with passive acoustic monitoring, and adds to a growing number of studies that have used passive acoustic monitoring to estimate population size. Although passive acoustic monitoring under-estimated true population parametersfeatures, likely due to the high similarity among many male songs, our findings suggest that autonomous recorders can provide reliable estimates of population size and demographic characteristicslongevity in a wild songbird.</p>
Amazonian manatee critical habitat revealed by artificial intelligence-based passive acoustic techniques
<p>These files are part of the publication under the same name. The pkl file contains manatee vocalizations mixed with background sounds that have been used to train a CNN. The resulting model is available in the .tflite file. The Python file can browse the vocalizations that have been made available, visualize the human labels, and if Tensorflow is available classify the samples, providing the classifier output for the manatee class. The corresponding paper should be referred to for thresholds and performance details that were used for field data.</p>
Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations
<p>This link provides the data from the playback experiment to determine effective detection areas for porpoises recorded by C-POD acoustic dataloggers. The firstdata set includes the artifically created porpoises click trains captured by the C-PODs and the second is the record of the rate of re-capture for the real, recorded porpoise clicks used in the experiment. </p>
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>
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>
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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Data and code from: Addressing widespread detection heterogeneity in avian occupancy modeling using passive acoustic surveys
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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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Passive acoustic monitoring indicates Barred Owls are established in northern coastal California and management intervention is warranted
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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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Blainville’s beaked whale (Mesoplodon densirostris) echolocation clicks from autonomous passive acoustic recordings
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Examples of killer whale (Orcinus orca) calls from passive acoustic monitoring in the Gulf of Alaska
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Vocal behavior in spotted seals (Phoca largha) and implications for passive acoustic monitoring
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Unsupervised acoustic classification of individual gibbon females and the implications for passive acoustic monitoring
<p>1. Passive acoustic monitoring (PAM) has the potential to greatly improve our ability to monitor cryptic yet vocal animals. Advances in automated signal detection have increased the scope of PAM, but distinguishing between individuals— which is necessary for density estimation— remains a major challenge. When individual identity is known, supervised classification techniques can be used to distinguish between individuals. Supervised methods require labeled training data, whereas unsupervised techniques do not. If the acoustic signals of individuals are sufficiently different, the number of clusters might represent the number of individuals sampled. The majority of applications of unsupervised techniques in animal vocalizations have focused on quantifying species-specific call repertoires. However, with increased interest in PAM applications, unsupervised methods that can distinguish between individuals are needed. <br> 2. Here, we use an existing dataset of Bornean gibbon female calls with known identity from five sites on Malaysian Borneo to test the ability of three different unsupervised clustering algorithms (affinity propagation, K-medoids, and Gaussian mixture model-based clustering) to distinguish between individuals. Calls from different gibbon females are readily distinguishable using supervised techniques. For internal validation of unsupervised cluster solutions, we calculated silhouette coefficients. For external validation, we compared clustering results with female identity labels using a standard metric: normalized mutual information. We also calculated classification accuracy by assigning unsupervised cluster solutions to females based on which cluster had the highest number of calls from a particular female.<br> 3. We found that affinity propagation clustering consistently outperformed the other algorithms for all metrics used. In particular, classification accuracy of affinity propagation clustering was more consistent as the number of females increased, and when we randomly sampled females across sites. <br> 4. We conclude that unsupervised techniques may be useful for providing additional information regarding individual identity for PAM applications. We stress that although we use gibbons as a case study, these methods will be applicable for any individually-distinct vocal animal. <br> </p>
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