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130 results for “acoustic monitoring”
Acoustic OSA Prediction During Wakefulness and Monitoring During Sleep
ClinicalTrials.gov study NCT04112927. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Intraoperative Neuromuscular Monitoring and Its Impact on Pre- and Postoperative Acoustic Outcomes in Thyroid Surgery
ClinicalTrials.gov study NCT06254859. IPD Sharing: NO. Countries: 1. Publications: 0.
Accuracy of Acoustic Rainbow Monitoring (ARM) Sensor
ClinicalTrials.gov study NCT03124862. IPD Sharing: NO. Countries: 1. Publications: 0.
An Open Label Placebo Study To Assess The Inhalation Profile Obtained By Acoustic Monitoring In COPD Patients Using The NEXThaler DPI
ClinicalTrials.gov study NCT02018549. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Rainbow Acoustic Monitoring (RAM) Clinical Engineering Data Collection
ClinicalTrials.gov study NCT03120897. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Using acoustic monitoring to assess insectivorous bat richness and activity in a sub-tropical savanna
<p><span>Understanding insectivorous bat diversity and activity is crucial for conservation efforts, particularly in under-researched regions like sub-tropical savannas. This study aimed to assess bat species richness and seasonal activity in the MalaMala Game Reserve, located in the southern region of the Greater Kruger National Park (KNP), South Africa. We conducted acoustic monitoring using ultrasonic detectors over two distinct seasons: the wet season (January to March) and the dry season (August) in 2022. Species identification was performed using Kaleidoscope software, complemented by manual verification to minimize misidentifications, particularly for species with overlapping echolocation characteristics. Our findings revealed 16 species across six families, representing 40% of the bat species known in KNP. The Molossidae family was the most dominant, followed by Vespertilionidae, while the Hipposideridae family recorded the fewest calls, likely due to their high-frequency echolocation calls that attenuate rapidly. Notably, our findings aligned with previous research indicating year-round presence of insectivorous bats in savanna woodlands. Seasonal variations in bat activity were observed, with significantly higher activity during the wet season, likely due to increased insect abundance and reduced thermoregulatory costs. Our study provides a critical benchmark for future bat research in the Greater KNP landscape, highlighting the importance of continued monitoring to detect changes in bat populations and inform conservation strategies.</span></p>
Fig. 5 in Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring
Fig. 5.—Temporal occurrence pattern of the Antarctic blue whale song at each of the five hydrophones during the seasonal periods when the song was recorded in the South Taranaki Bight in New Zealand during 2016 (left panels) and 2017 (right panels). The y-axis represents the number of hours per day during which blue whale song was detected, and the x-axis represents the date. Grayed out sections represent gaps in recording due to hydrophone refurbishment. Synchronous peaks in occurrence between hydrophones and the overlapping detection ranges of the hydrophones (MARU1–5) indicate calls are detected simultaneously at multiple locations.
Towards Using Virtual Acoustics for Evaluating Spatial Ecoacoustic Monitoring Technologies - Data
<h1>About</h1> <p>This database contains the raw data and certain outputs used for the project 'Towards Using Virtual Acoustics for Evaluating Spatial Ecoacoustic Monitoring Technologies'. In this work, we developed an ambisonic Virtual Sound Environment (VSE) for simulating real natural soundscapes to evaluate spatial PAM technologies in a more controlled and repeatable manner.</p> <p>We set three objectives to validate this approach:</p> <ul> <li>(O1) to determine whether the VSE could replicate natural soundscapes well enough to be a test environment;</li> <li>(O2) to pilot the VSE as a test environment for Passive Acoustic Monitoring (PAM) hardware; and, </li> <li>(O3) to pilot the VSE as a test platform for PAM software.</li> </ul> <p>To meet these objectives, we used a recently-developed, open source six-microphone field recorder to capture recordings of six field sites and their VSE-based simulations. Sites were based at the Imperial College Silwood Park Campus (Ascot, UK). For O1, we compared field and VSE recordings using a typical suite of ecoacoustic metrics. For O2, we used the VSE to explore how orientation impacts the performance of the six-microphone array. We extended the suite of metrics from O1 to compare VSE recordings from this array at various pitch angles: vertical (as in the field), 45° pitch, and horizontal. For O3, we investigate how BirdNET and HARKBird, software for classifying and localising avian calls, respectively, perform on bird calls added to the VSE-replicated soundscapes. We compare adding calls by encoding to the ambisonics domain and by playback from individual loudspeakers. </p> <p> </p> <h3><strong>The data is organised as follows: </strong></h3> <p><strong>'6mic Audio O1O2': </strong>contains the six-channel field and VSE-based recordings of all six sites. Recordings are approximately 10 minutes long. Note that there are three VSE-based recording per site, for each recording orientation used in the VSE (vertical, 45°, and horizontal). Several of our analyses used low-passed versions of these recordings (4 kHz cutoff and 12 dB roll-off); note that the recordings provided here are raw and therefore not low-passed.</p> <ul> <li>File names indicate whether the recording was performed in the field or VSE ('Field' vs 'ReRec') and the orientation of the 6mic array for the latter ('V', '45', or 'H').</li> <li>The final number in each filename indicates the Site the recording corresponds to (1-6).</li> </ul> <p><strong>'6mic Audio O3':</strong> contains the six-channel VSE recordings of five of the original sites with additional avian calls located at certain moments in space and time in each recording. 10 bird calls were added to the soundscapes, each one in its own soundscape recording (each of the five sites' soundscapes was therefore used twice). Four methods of adding the bird calls were trialled, hence there are 40 files in this folder – 10 for each of: ambisonic encoding, playback from individual loudspeakers, playback from individual loudspeakers with no reverb, and playback from individual loudspeakers with no reverb or background soundscape (i.e., 'soloed'). Our analyses focussed on comparing the first two of these methods. The filenames are structured as follows:</p> <ul> <li> "sXsYbirdNameEmbeddingMethod', where X corresponds to the site the background soundscape was recorded at,</li> <li>Y indicates the position of the sinusoidal sweep used to create simulated reverb of the bird call (1-4 for 0°, 90°, 180°, or 270° around the device used to capture the ambisonic soundscape recordings),</li> <li>'birdName' is the common name of the added species, and</li> <li>'EmbeddingMethod' is either: <ul> <li>'Ambi' (ambisonic encoding),</li> <li>'LSPK' (playback from an individual loudspeaker),</li> <li>'LSPK-NR' (playback from an individual loudspeaker with no reverb), or</li> <li>'Solo' (soloed playback from an individual loudspeaker).</li> </ul> </li> <li>Note that due to issues of spatial aliasing, we low-passed the 'Ambi' and 'LSPK' recordings in our analyses (as the two main embedding methods compared) using a 4 kHz cutoff and 12 dB roll-off. However, again the raw (not low-passed) audio is shared here.</li> </ul> <p><strong>'Acoustic Indices':</strong> contains the CSV files with matrices of the Acoustic Indices extracted from the first channel of the low-passed '6mic Audio O1O2' recordings.</p> <ul> <li>Each column is for a different index, rows are the values over time (indices were extracted on 30 s windows).</li> <li>We extracted the following 7 common acoustic indices: <ul> <li>Acoustic Complexity Index (ACI; column 1),</li> <li>Acoustic Diversity Index (ADI; column 2),</li> <li>Acoustic Evenness (AEve; column 3),</li> <li>Bioacoustic Index (Bio; column 4),</li> <li>Normalised Difference Soundscape Index (NDSI; column 5)</li> <li>Acoustic Entropy (H; column 6), and</li> <li>Median of the Amplitude Envelope (M; column 7).</li> <li>Filenames indicate: <ul> <li>the field site,</li> <li>whether the indices are for a field or VSE ('Lab') recording, and</li> <li>the orientation of the recording for the latter ('Vert', '45', or 'H').</li> <li>The 'LP' suffix denotes that these indices were extracted from a low-passed version of the 6mic O1O2 recordings (4 kHz cutoff and 12 dB roll-off).</li> </ul> </li> </ul> </li> </ul> <p><strong>'BirdNET O1O2 Outputs':</strong> CSV files generated from avian call classifier BirdNET (using the Winows GUI version) using the first channel ('Mic 1') of the '6mic Audio O1O2' recordings.</p> <p><strong>'BirdNET O3 Outputs':</strong> outputs of BirdNET on the '6mic Audio O3' recordings.</p> <ul> <li>Here, rather than the raw CSV files generated by BirdNET, the BirdNET results have been filtered to just those during the added bird calls' start and end times, and have been compiled into two CSV files: <ul> <li>one for recordings of VSE-based soundscapes with bird calls added by <em>ambisonic encoding</em> and</li> <li>another for calls added by <em>individual loudspeaker playback</em>.</li> </ul> </li> <li>These CSV files also contain columns to specify added birds' site, sweep (used to generate reverberation, see '6mic Audio O3' above), azimuth and elevation.</li> </ul> <p><strong>'HARKBird O1O2 Outputs':</strong> outputs of avian call localisation tool HARKBird on the '6mic Audio O1O2' recordings. Note that HARKBird outputs a folder with additional results for each file, however, only the CSV files presented here. These contain the times and estimated azimuth angles of bird calls and were the only HARKBird ouput used for subsequent analyses.</p> <ul> <li>Filenames indicate whether recordings are from the field ('Field') or VSE ('ReRec'), and</li> <li>those for the latter also indicate the recording orientation ('Vert', 45' 'H').</li> <li>The final number in each filename indicates the site the recording corresponds to.</li> </ul> <p><strong>'HARKBird O3 Outputs':</strong> CSV files of HARKBird outputs (as described above) for the '6mic Audio O3' recordings.</p> <ul> <li>Filenames are based on those for this set of recordings (see '6mic Audio O3' above);</li> <li>names that contain 'LP' were low-passed (with a 4 kHz cutoff frequency and 12 dB roll-off) prior to passing through HARKBird.</li> </ul> <p><strong>'Manual Labels O2':</strong> manual labels of audible bird calls' species in the omnidirectional (first) channel of the Zylia ZM-1 ambisonic recordings (see 'Zylia Recordings' below) for all sites. This data was used to calculate the precision and recall of BirdNET's outputs on the 6mic O1O2 recordings for these sites.</p> <p><strong>'VGGish Features':</strong> contains the 128-dimension feature embedding of the pre-trained VGGish convolutional neural network extracted from the '6mic Audio O1O2' recordings.</p> <ul> <li>Filenames indicate the site,</li> <li>whether the recording was made in the field or VSE ('Lab'),</li> <li>and the recording's orientation.</li> <li>Note again that recordings were low-passed (4 kHz cutoff and 12 dB roll-off) prior to the feature extraction, hence the 'LP' suffix.</li> </ul> <p><strong>'Zylia Recordings':</strong> approximately 10 minute field recordings of the 6 study sites captured with the 19-microphone ZYLIA 'ZM-1' recorder. These recordings have been converted to third order ambisonic 'b-format' (16 channels) using Furse-Malham channel ordering and SN3D normalisation. This was achieved with the 'Zylia Ambisonics Converter' software. These third-order ambisonic recordings were used to replicate the six sites' soundscapes in the VSE.</p> <p>The accompanying code for this dataset has been submitted via ScholarOne with the manuscript for peer-review.</p>
Labeled passive acoustic monitoring dataset from Danum Valley Conservation Area, Sabah, Malaysia
<p>This is the data archive for labeled training, validation and test data from a passive acoustic monitoring project in Danum Valley Conservation Area, Sabah, Malaysia. Please refer to Clink et al. (2023) and our <a href="https://github.com/DenaJGibbon/Workflow-for-automated-detection-and-classification-gibbon-calls">GitHub Page</a> for details and code.</p> <p>"AnnotatedFilesTest"</p> <p>"AnnotatedFilesValidation"</p> <p>"TestSoundFiles"</p> <p>"TrainingFilesValidated"</p> <p>"TrainingFilesValidatedAddFemales"</p> <p>"TrueFalsePositives"</p> <p>"UpdatedDanumDetectionsHQ99" </p> <p> "ValidationSoundFiles" </p>
Supporting Data for "Seismic Monitoring of Rockfalls Using Fiber-Optic Distributed Acoustic Sensing"
<p>This file contains data and MATLAB scripts to generate the figures presented in the manuscript "Seismic Monitoring of Rockfalls Using Fiber-Optic Distributed Acoustic Sensing".</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
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.