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42 results for “Passive acoustic monitoring”
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 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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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>
Data from: Persistent near real-time passive acoustic monitoring for baleen whales from a moored buoy: system description and evaluation
1. Managing interactions between human activities and marine mammals often relies on an understanding of the real-time distribution or occurrence of animals. Visual surveys typically cannot provide persistent monitoring because of expense and weather limitations, and while passive acoustic recorders can monitor continuously, the data they collect are often not accessible until the recorder is recovered. 2. We have developed a moored passive acoustic monitoring system that provides near real-time occurrence estimates for humpback, sei, fin, and North Atlantic right whales from a single site for a year, and makes those occurrence estimates available via a publicly accessible website, email and text messages, a smartphone/tablet app, and the U.S. Coast Guard's maritime domain awareness software. We evaluated this system using a buoy deployed off the coast of Massachusetts during 2015-2016 and redeployed again during 2016-2017. Near real-time estimates of whale occurrence were compared to simultaneously collected archived audio as well as whale sightings collected near the buoy by aerial surveys. 3. False detection rates for right, humpback, and sei whales were 0% and nearly 0% for fin whales, while missed detection rates at daily time scales were modest (12-42%). Missed detections were significantly associated with low calling rates for all species. We observed strong associations between right whale visual sightings and near real-time acoustic detections over a monitoring range of 30-40 km and temporal scales of 24-48 hours, suggesting that silent animals were not especially problematic for estimating occurrence of right whales in the study area. There was no association between acoustic detections and visual sightings of humpback whales. 4. The moored buoy has been used to reduce the risk of ship strikes for right whales in a U.S. Coast Guard gunnery range, and can be applied to other mitigation applications.
Raw data for Evaluating community-wide temporal sampling in passive acoustic monitoring: A comprehensive study of avian vocal patterns in subtropical montane forests
<p>This dataset, utilized in the research paper "<a href="https://doi.org/10.12688/f1000research.141951.1">Evaluating community-wide temporal sampling in passive acoustic monitoring: A comprehensive study of avian vocal patterns in subtropical montane forests</a>", comprises columns such as site_name, longitude (WGS84), latitude (WGS84), altitude (meters above sea level), vegetation types, date, hour, minute, julian_day, scientific_name, and Vocal Activity Rate per minute (VAR_m). It encompasses data gathered from twelve Passive Acoustic Monitoring (PAM) stations positioned within Yushan National Park (YSNP), Taiwan. The collection period spanned from March 1 to June 30, 2021. The dataset documents 8,202,731 vocalizations from twelve bird species, detected using an automated sound identification tool named SILIC (Sound Identification and Labeling Intelligence for Creatures). The vocalization data is aggregated by site, species, and time (down to the minute).</p>
Fig. 3 in Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring
Fig. 3.—Temporal occurrence pattern of New Zealand (dark blue), Antarctic (red), and Australian (yellow) blue whale song detections at each of the five hydrophones. The y-axis represents the number of hours per day that blue whale song was detected, and the x-axis represents the recording period. Grayed out sections represent gaps in recording due to hydrophone refurbishment.
Fig. 2 in Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring
Fig. 2.—Example spectrograms of the three song types recorded in New Zealand waters. (A) New Zealand song on 31 May 2016, (B) Antarctic song on 26 August 2017, and (C) Australian song on 22 January 2017. Spectrograms are configured with a 2048-point fast Fourier transform, Hann window, 50% overlap.
Fig. 4 in Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring
Fig. 4.—Conceptual map illustrating the current understanding of the approximate typical range of each blue whale population. Colors indicate the song type, and patterns represent inferred ecological use of each region. Distribution and occurrence patterns are synthesized from acoustic research published in the literature (e.g., Stafford et al. 2004; Balcazar et al. 2015, 2017; Tripovich et al. 2015; McCauley et al. 2018; Warren et al. 2021), and findings presented in this study.
Fig. 1 in Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring
Fig. 1.—Map of the study area in the South Taranaki Bight region, with hydrophone locations denoted by the stars. Gray lines show bathymetry contours at 50-m depth increments, from 0 to 500 m. Location of the study area within New Zealand is indicated by the inset map.
Data from: Persistent near real-time passive acoustic monitoring for baleen whales from a moored buoy: system description and evaluation
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Unsupervised acoustic classification of individual gibbon females and the implications for passive acoustic monitoring
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Automated detection of Hainan gibbon calls for passive acoustic monitoring
<p>Data accompanying the paper: "Automated detection of Hainan gibbon calls for passive acoustic monitoring"</p> <p><strong>Please cite this dataset as:</strong></p> <blockquote> <p>Dufourq, Emmanuel and Durbach, Ian and Hansford, James and Hoepfner, Amanda and Ma, Heidi and Bryant, Jessica and Stender, Christina and Li, Wenyong and Liu, Zhiwei and Chen, Qing and Zhou, Zhaoli and Turvey, Samuel. (2020). Automated detection of Hainan gibbon calls for passive acoustic monitoring. BioRxiv doi: https://doi.org/10.1101/2020.09.07.285502</p> </blockquote> <p>The Hainan gibbon is the world's rarest primate and one of the world's rarest mammals, with only a single population of about 30 individuals surviving in Bawangling National Nature Reserve (BNNR), Hainan, China. Eight Song Meter SM3 recorders (Wildlife Acoustics, Maynard, Massachusetts) were used to collect acoustic data from 1 March to 20 August 2016 within BNNR. Recorders were attached to trees at approximately 1.5 meters from the ground in tropical evergreen forest. Recorders were set to record for eight hours each day from the time of sunrise, which varied between approximately 05:00 and 06:00 during the study period. Devices did not record continuously throughout the entire survey period due to logistical and technical issues; in total, survey days per recorder varied between 79 and 129 days, and roughly 6,000 hours of recordings were collected. The majority of recordings were made with a sampling rate of 9,600Hz and bit depth of 16, with isolated recordings at 28,800Hz.</p> <p>We provide the audio data (.wav) used to train and test our neural network classifier along with the corresponding labelled text files (.data).</p> <p><strong>Files provided</strong></p> <p>Train.zip - contains the training .wav audio files</p> <p>Train_Labels.zip - contains the labels for the training data</p> <p>Test.zip - contains the testing .wav audio files</p> <p>Test_Labels.zip - contains the labels for the test data</p> <p>Extra_Labelled_Data.zip - contains extra data that was labelled and non-gibbon calls used for training</p> <p>Extra_Labels.zip - contains the labels for the extra labelled data</p> <p>Unlabelled_Data.zip - contains additional .wav audio files which have not been labelled. These are split into various files (1-15) and can be downloaded individually.</p> <p>Code.zip - contains all the software scripts and notebooks</p> <p>Manual-zip - contains the user manual</p> <p><strong>Labels</strong></p> <p>The names of the labelled files start with either "g_" or "n_", for example "g_HGSM3D_0+1_20160429_051600.data" and "n_HGSM3D_0+1_20160429_051600.data". Files starting with "g_" contain the timestamps of the gibbon calls, and files starting with "n_" contain the timestamps of non-gibbon calls (e.g. background noise and bird calls). An audio file will have both a "g_" and "n_" file. Each file has the following format: Start,End,Duration,Type,Notes, where "start" denotes the start time in seconds, "end" denotes the end time in seconds, "duration" denotes the duration (end - start) in seconds, "type" denotes the type of call/noise and "notes" are additional notes which we labelled.</p> <p><strong>Types</strong></p> <p>The legend for the "type" column in the labelled files is defined as follows. The types for gibbon and non-gibbon files are different and we distinguish this below.</p> <p> </p> <p>Gibbon files ("g_")</p> <p>type 1 = one pulse gibbon call</p> <p>type 2 = multiple pulse gibbon call (check "notes column" below)</p> <p>type 3 = duet gibbon call</p> <p> </p> <p><em>Notes column (only available in gibbon files)</em></p> <p>One of the following: 2 pulse call, 3 pulse call, 4 pulse call, 5 pulse call or 6 pulse call.</p> <p> </p> <p>Non-gibbon files ("n_")</p> <p>type 1 = rain</p> <p>type 2 = other species (e.g. birds)</p> <p>type 3 = rain and other species</p> <p>type 4 = rain and external noise (e.g. aircraft)</p> <p>type 5 = natural sounds and external noise</p> <p>type 6 = natural sounds and other species</p> <p><strong>Training files (containing gibbon calls):</strong></p> <p>HGSM3AC_0+1_20160309_055600<br> HGSM3AC_0+1_20160312_055400<br> HGSM3A_0+1_20160304_060000<br> HGSM3BD_0+1_20160305_060000<br> HGSM3AC_0+1_20160314_055200<br> HGSM3B_0+1_20150616_050500<br> HGSM3BD_0+1_20160402_053600<br> HGSM3D_0+1_20160429_051600<br> HGSM3B_0+1_20160305_060000<br> HGSM3C_0+1_20160501_051500<br> HGSM3SOL_0+1_20160320_054700<br> HGSM3SOL_0+1_20160405_053400<br> HGSM3BD_0+1_20160401_053700</p> <p><strong>Testing files:</strong></p> <p>HGSM3B_0+1_20160323_054500<br> HGSM3B_0+1_20160321_054700<br> HGSM3B_0+1_20160306_055900<br> HGSM3B_0+1_20160308_055700<br> HGSM3B_0+1_20160309_055600<br> HGSM3B_0+1_20160316_055100<br> HGSM3B_0+1_20160311_055500<br> HGSM3B_0+1_20160304_060000<br> HGSM3B_0+1_20160322_054600</p>
Data from: Passive acoustic monitoring effectively detects Northern Spotted Owls and Barred Owls over a range of forest conditions
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Gunshot sound files and spectrogram images from passive acoustic monitoring data in Vietnam.
<h3><strong>Introduction</strong></h3> <p>This dataset was created to support benchmarking of automated gunshot detection models using 'torch for R'. The data includes recordings collected from Chu Mom Ray National Park in Vietnam combined with an open dataset of gunshots from Belize (Katsis et al. 2022). The dataset is organized into several folders containing .jpg images and their corresponding .wav audio clips. </p> <h3>Data Summary</h3> <p>The dataset is divided into three main categories based on the region and use case:</p> <ol> <li>imagesvietnamunbalanced: Contains spectrogram images and audio data collected from Chu Mom Ray National Park in Vietnam. These data are used for training and evaluating automated gunshot detection models.</li> <li>imagesvietnam_belize: Includes spectrogram images and audio data from both Vietnam and Belize. The data in this folder is used for benchmarking model performance across different geographical regions.</li> <li>testdatacombined: Separate test data to evaluate performance with a large number of noise clips, representing real-world automated detection scenarios.</li> </ol> <p>Each .jpg image in these directories is associated with a .wav file representing a corresponding audio clip. The .wav files were recorded using passive acoustic monitoring and clips were isolated using manual annotations in Raven Pro Software.</p> <p>If used please cite: </p> <p>Vu, T. T., Phan, D. V., Le, T. S., & Clink, D. J. (2024). Investigating hunting in a protected area in Southeast Asia using passive acoustic monitoring with mobile smartphones and deep learning. <em>Ecological Indicators. </em></p> <p>Vu, T. T., Phan, D. V., Le, T. S., & Clink, D. J. (2024). Gunshot sound files and images from passive acoustic monitoring data in Vietnam. [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13893977" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13893977</a></p> <p>Katsis, Lydia; Hill, Andrew; Piña-Covarrubias, Evelyn; Prince, Peter; Rogers, Alex; Doncaster, C. Patrick; Snaddon, Jake (2022), “Tropical forest gunshot classification training audio dataset”, <em>Mendeley Data</em>, V3, doi: 10.17632/x48cwz364j.3</p>
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