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53 results for “sound recordings”
A in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
A street in downtown Delhi9
Data from: COPD screening using time–frequency features of self-recorded respiratory sounds
Open the record for dataset details and reuse information.
Data from: The Chirocopter: a UAV for recording sound and video of bats at altitude
1. Most recordings of bats are conducted with fixed equipment, which relies on opportunistic data collection. Unmanned aerial vehicles (such as drones) are considered inappropriate for recording bats due to ultrasound noise constraints. 2. We developed a UAV system that physically isolates UAV noise so we can record, with 3D maneuverability, ultrasonic audio and spatial thermal data of bat flight at altitude. 3. We tested the noise of our UAV with various payloads and microphone configurations to characterize the ultrasonic noise of our system, physically isolate drone noise from the microphone, and maximize UAV flight performance. 4. Over 84 minutes of recordings, we captured 3,847 echolocation signals from bats with corresponding thermal data of bat flight. Our system provides a feasible mechanism to capture both acoustic and video data of bats aloft at flexible locations and altitudes. 5. We include information on how to extend our method to apply to acoustic recordings in the audible (20 Hz-20 kHz) range for recording sounds of other taxa.
Data from: Long-term sound and movement recording tags to study natural behaviour and reaction to ship noise of seals
The impact of anthropogenic noise on marine fauna is of increasing conservation concern with vessel noise being one of the major contributors. Animals that rely on shallow coastal habitats may be especially vulnerable to this form of pollution. Very limited information is available on how much noise from ship traffic individual animals experience, and how they may react to it due to a lack of suitable methods. To address this, we developed long‐duration audio and 3D‐movement tags (DTAGs) and deployed them on three harbor seals and two gray seals in the North Sea during 2015–2016. These tags recorded sound, accelerometry, magnetometry, and pressure continuously for up to 21 days. GPS positions were also sampled for one seal continuously throughout the recording period. A separate tag, combining a camera and an accelerometer logger, was deployed on two harbor seals to visualize specific behaviors that helped interpret accelerometer signals in the DTAG data. Combining data from depth, accelerometer, and audio sensors, we found that animals spent 6.6%–42.3% of the time hauled out (either on land or partly submerged), and 5.3%–12.4% of their at‐sea time resting at the sea bottom, while the remaining time was used for traveling, resting at surface, and foraging. Animals were exposed to audible vessel noise 2.2%–20.5% of their time when in water, and we demonstrate that interruption of functional behaviors (e.g., resting) in some cases coincides with high‐level vessel noise. Two‐thirds of the ship noise events were traceable by the AIS vessel tracking system, while one‐third comprised vessels without AIS. This preliminary study demonstrates how concomitant long‐term continuous broadband on‐animal sound and movement recordings may be an important tool in future quantification of disturbance effects of anthropogenic activities at sea and assessment of long‐term population impacts on pinnipeds.
Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data
<p>Occupancy modeling is used to evaluate avian distributions and habitat associations, yet it typically requires extensive survey effort because a minimum of three repeat samples are required for accurate parameter estimation. Autonomous recording units (ARUs) can reduce the need for surveyors on site, yet ARUs utility were limited by hardware costs and the time required to manually annotate recordings. Software that identifies bird vocalizations may reduce expert time needed, if classification is sufficiently accurate. We assessed the performance of BirdNET – an automated classifier capable of identifying vocalizations from >900 North American and European bird species – by comparing automated to manual annotations of recordings of 13 breeding bird species collected in northwestern California. We compared the parameter estimates of occupancy models evaluating habitat associations supplied with manually annotated data (9 min recording segments) to output from models supplied with BirdNET detections. We used three sets of BirdNET output to evaluate the duration of automatic annotation needed to approach manually annotated model parameter estimates: 9-min, 87-min, and 87-min of high-confidence detections. We incorporated 100 3-sec manually validated BirdNET detections per species to estimate true and false positive rates within an occupancy model. BirdNET correctly identified 90% and 65% of the bird species a human detected when data were restricted to detections exceeding a low or high confidence score threshold, respectively. Occupancy estimates, including habitat associations, were similar regardless of method. Precision (proportion of true positives to all detections) was >0.70 for 9 of 13 species, and a low of 0.29. However, processing of longer recordings was needed to rival manually annotated data. We conclude that BirdNET is suitable for annotating multispecies recordings for occupancy modeling when extended recording durations are used. Together, ARUs and BirdNET may benefit monitoring and, ultimately, conservation of bird populations by greatly increasing monitoring opportunities. </p>
Urban background sound recordings for virtual acoustics under various weather conditions at IHTApark
<p>This is an open database of calibrated background sound recordings with metadata on meteorological and acoustical parameters ready to be used in virtual reality applications, for soundsacpe studies.</p> <p>The soundscapes have been recorded at the IHTApark (green space next to the Institute for Hearing Technology and Acoustics) in Aachen (Germany) in winter and spring of 2022.</p> <p>They have been segemented into 30-seconds auralizable snippets.</p> <p>For an<strong> <a href="https://paad-group.github.io/IHTApark-ambient-recordings/" target="_blank" rel="noopener">interactive exploration</a></strong> of the soundscapes, please visit the follwing website:</p> <p><a href="https://paad-group.github.io/IHTApark-ambient-recordings/" target="_blank" rel="noopener"></a></p> <p><a href="https://paad-group.github.io/IHTApark-ambient-recordings/" target="_blank" rel="noopener">https://paad-group.github.io/IHTApark-ambient-recordings/</a></p> <p>The dabase is available as binaural files, first-order ambisonics files, and as omni files.</p> <p>The metadata is included as a .xlsx file.</p> <p> </p>
Data from: AviaNZ: a future-proofed program for annotation and recognition of animal sounds in long-time field recordings
The routine collection of long‐time acoustic recordings of animals in the field presents new challenges in data analysis. While many terabytes of data are collected annually, effective use of this noisy, highly variable data require skilled humans to manually identify calls. While computer programs to automatically analyse these recordings are becoming available, it is important that they are user‐friendly and easy‐to‐use, so that everybody – citizen scientists, wildlife managers, researchers – can take advantage of them, and that they keep the human in the loop so analyses carried out this year are comparable both to manual call counts from the past, and more accurate automated analyses performed in the future. We present the AviaNZ program, which is designed to achieve these goals: the software includes methods for simple, rapid manual annotation of recordings, denoising and segmentation methods, and a training procedure by which the user can prepare their own filters to automatically recognize individual species. The software can run in batch mode, automatically processing folders of field recordings, and then present the outputs to enable the quick and easy review of the results. Finally, the outputs are presented in a variety of spreadsheets to enable different statistical analyses to be performed. We describe the various workflows of manually and semi‐automatically processing sound files, annotating them to train automatic filters, using those filters in batch mode, and how the software facilitates rapid evaluation of the automated analysis. A demonstration of the software, comparing manual and automatic detection of calls of the little spotted kiwi Apteryx owenii is given. It shows that while the automatic detection does produce false positives, human correction of these is far faster than manual review of the whole sound file. AviaNZ is a freely available open‐source standalone program. Our experience shows that it can be used by anybody quickly and easily. However, for experienced users it is easily customizable and extendable. By enabling everybody involved with acoustic bird recording to quickly and easily analyse their own data, while future‐proofing it by keeping the human in the loop, we are enabling acoustic field recordings to meet their potential.
Fig. 4. A in Identification of Sound-Producing Hydrophilid Beetles (Coleoptera: Hydrophilidae) in Underwater Recordings Using Digital Signal Processing
Fig. 4. A half-second vocalization by Tropisternus blatchleyi from 0–5,000 Hz changed into the frequency domain using the Fast Fourier Transformation. Active call frequency regions are at 1,100 Hz and 4,400 Hz. The first feature for T. blatchleyi divides the sum of the points in the active frequency band by the sum of the points in the inactive band, yielding a large number in T. blatchleyi exemplar calls.
Fig. 3 in Identification of Sound-Producing Hydrophilid Beetles (Coleoptera: Hydrophilidae) in Underwater Recordings Using Digital Signal Processing
Fig. 3. The classifier algorithm with two features shown in a two-dimensional graph. Beetle call data and noise data are classified based on two beetle call features: difference in active frequency range shape (x-axis and in Matlab™ as a template) and a ratio of amplitudes in an active and non-active frequency range (y-axis). The algorithm is shown as a solid black line. Most beetle calls fall within the correct classification in the lower left-hand corner, however, some fall outside the equation and are classified as noise. Likewise, noises are occasionally classified as beetles. As more features are added, the algorithm becomes multidimensional.
Fig. 2 in Identification of Sound-Producing Hydrophilid Beetles (Coleoptera: Hydrophilidae) in Underwater Recordings Using Digital Signal Processing
Fig. 2. Five distress calls by Berosus pantherinus transformed into the frequency domain using the fast fourier transformation from 0–12,000 Hz (x-axis). Wide active frequency bands can be seen from 1,500–6,000 Hz and 7,000–9,500 Hz. The feature for distress calls is the sum of the data points between 1,000–6,000 Hz with an amplitude (y-axis) greater than 2.
FIGURE 5 in From an old sound recording to a new species in the genus Horatosphaga (Orthoptera: Tettigonioidea: Phaneropterinae: Acrometopini)
FIGURE 5. Distribution of intervals between the teeth of the stridulatory files in the holotypes of H. raggei n. sp., H. stuhlmanni and H. leggei (interval in the middle of the file set to 1 in each species).
FIGURE 1 in From an old sound recording to a new species in the genus Horatosphaga (Orthoptera: Tettigonioidea: Phaneropterinae: Acrometopini)
FIGURE 1. Distribution of H. stuhlmanni and H. raggei n. sp. (squares H. stuhlmanni, blue: data from Ragge 1960, red: from La Baume 1911, diamond H. raggei n. sp., circles type localities).
HEARt Sounds: Audio Recordings to Improve Discharge Communication for Cardiology Inpatients
ClinicalTrials.gov study NCT03735342. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Long-term sound and movement recording tags to study natural behaviour and reaction to ship noise of seals
Open the record for dataset details and reuse information.
Data from: The Chirocopter: a UAV for recording sound and video of bats at altitude
Open the record for dataset details and reuse information.
Data from: AviaNZ: a future-proofed program for annotation and recognition of animal sounds in long-time field recordings
Open the record for dataset details and reuse information.
Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data
Open the record for dataset details and reuse information.
Sound recordings of courtship displays of Allen's (Selasphorus sasin), Rufous (S. rufus), and Hybrid (S. sasin x S. rufus) hummingbirds recorded between 2014 and 2021 in California, Oregon, and Alaska
Open the record for dataset details and reuse information.
Thomas J. Walker Sound Recordings from Macaulay Library of Natural Sounds: Thomas J. Walker__s insect recordings
This resource includes a large selection of the recordings deposited in the Macaulay Library of Natural Sounds by Thomas J. Walker. These recordings are mainly calling songs of crickets and katydids from North America north of Mexico and from the Caribbean.
Filming the sound: Anomaly Detection on Audio Tape Recordings using Computer Vision Algorithms
<p>This repository makes available the dataset related to the paper:</p> <p>Zafer Çınar, Alessandro Russo, Matteo Spanio, Niccolò Pretto, and Sergio Canazza, <em>Filming the Sound: Anomaly Detection on Audio Tape Recordings using Computer Vision Algorithms</em>, IAI4CH, Bozen, 2024.</p> <p>The dataset and the experiment are described in the publication above.</p> <p>This repository contains two main directories (<strong>bold</strong> indicates directory names):</p> <ul> <li><strong>video samples</strong>: the actual videos used in the paper's experiment. This folder contains four subdirectories - 3.75 ips, 7.5 ips, 15 ips, and 30 ips - each representing a different playback speed (in inches per second). Within each subdirectory are several MP4 files, recorded on an A810 Studer open reel recorder, documenting the playback of magnetic audio tapes. The files follow the naming convention “Xips (Y).mp4,” where <em>X</em> represents the tape playback speed and <em>Y</em> is a serial number identifier for each video.</li> <li><strong>irregularities</strong>: the metadata for each video with timestamp and type of irregularity. The folder includes four CSV files - 3.75.csv, 7.5.csv, 15.csv, and 30.csv - corresponding to the playback speeds of the video samples. Each CSV file provides handmade annotations for its respective videos, with three columns: <ul> <li><em>video_id</em>: name of the video file in the format “Xips (Y).mp4,” where <em>X</em> is the tape speed and <em>Y</em> is the ID number.</li> <li><em>time_label</em>: timestamp indicating the irregularity, formatted as HH:MM:SS.mls.</li> <li><em>irregularity_type</em>: category of the detected anomaly, which may be one of the following: “splice,” “shadow,” “end-of-tape,” or “annotation.”</li> </ul> </li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.