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1,524 results for “acoustics”
Figure 7 in Underwater acoustic behavior of bearded seals (Erignathus barbatus) in the northeastern Chukchi Sea, 2007-2010
Figure 7. Proportion of bearded seal calls for all overwinter 2007–2008 (A) and overwinter 2008–2009 (B) recording stations (samples were 10 min long and recorded every 10th day).
Figure 6 in Underwater acoustic behavior of bearded seals (Erignathus barbatus) in the northeastern Chukchi Sea, 2007-2010
Figure 6. Diel pattern of bearded seal calls combined across all summer 2009 (A) and summer 2010 (B) recording stations. Vertical bars represent the percentage of time bins that have calls present in each 1 h time bin. Horizontal bars indicate periods of daylight (white), periods of darkness (dark gray), and periods of daylight or darkness depending on the time of recording (light gray).
Figure 2 in Underwater acoustic behavior of bearded seals (Erignathus barbatus) in the northeastern Chukchi Sea, 2007-2010
Figure 2. Bearded seal calls representing the major call types found in the Chukchi Sea dataset. Call types are modified from Risch et al. (2007). See Table 2 for call type definitions.
Fig. 2 in A new live trap for the acoustically orienting parasitoid fly Emblemasoma erro (Diptera: Sarcophagidae)
Fig. 2. Details of trap construction showing a) the trap box and b) the speaker box. To reveal internal components, the front-facing, side plywood panels of the trap and speaker boxes are not illustrated. Also, for clarity, only 3 of the 5 wire screen cones of the trap box are illustrated.
Fig. 1 in A new live trap for the acoustically orienting parasitoid fly Emblemasoma erro (Diptera: Sarcophagidae)
Fig. 1. The complete live trap deployed in the field. Captured flies are visible in the holding jar assembly at the top of the trap box.
Data set for "Bidirectional microwave-optical transduction based on integration of high-overtone bulk acoustic resonators and photonic circuits"
<p>The repository contains raw data, processing scripts, simulation and GDS files for the manuscript "Bidirectional microwave-optical transduction based on integration of high-overtone bulk acoustic resonators and photonic circuits". For detailed usage instructions, please take a look at the README.txt file. </p>
Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable"
<p>Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable" </p> <p><a href="../api/records/13133835/draft/files/tmdcm.txt/content" target="_blank" rel="noopener noreferrer">tmdcm.txt</a>: current meter data </p> <p><a href="../api/records/13133835/draft/files/tide.txt/content" target="_blank" rel="noopener noreferrer">tide.txt</a>: tidal gauge data </p> <p><a href="../api/records/13133835/draft/files/windspeed.txt/content" target="_blank" rel="noopener noreferrer">windspeed.txt</a>: windspeed data </p> <p>Figure 2: Figure2.npy</p> <p>Figure 3: Figure 3 abc .npy</p> <p>Figure16: <a href="../api/records/13133835/draft/files/spatial_Vc.npy/content" target="_blank" rel="noopener noreferrer">spatial_Vc.npy</a> & <a href="13133835" target="_blank" rel="noopener noreferrer">spatial_h.npy</a> </p> <p>Figure 17: <a href="../api/records/13133835/draft/files/streching_ncf.npy/content" target="_blank" rel="noopener noreferrer">streching_ncf.npy</a></p>
Statistical Test of Distance–Duality Relation with Type Ia Supernovae and Baryon Acoustic Oscillations (3rd version)
<p><strong>Summary</strong></p> <p>This package contains data and processing tools for replicating the research presented in the paper "Statistical Test of Distance–Duality Relation with Type Ia Supernovae and Baryon Acoustic Oscillations" (2018, ApJ, DOI: <a href="https://doi.org/10.3847/1538-4357/aac88f">10.3847/1538-4357/aac88f</a>, <a href="https://arxiv.org/abs/1604.04631">arXiv:1604.04631</a>).</p> <p>The compressed archive file "ddmc-nosample-v3.1.tar.xz" contains only the compressed SNIa data, the BAO measurements, and 3rd-party data files used in this work. The random samples can be re-created by the tools included in the package. This is the file suitable for low-speed download.</p> <p>The file "ddmc-v3.1.tar.xz" contains the full set of random sample output files and analysis results in addition to those in the "ddmc-nosample-v3.1.tar.xz" file. This is the archive containing all the data and figure files used directly in the paper.</p> <p>To uncompress the files, the XZ Utils software package is required.</p> <p>The file "CHECKSUM.asc" is a GPG-clearsigned text file containing the SHA-512 checksum values for file integrity verification. The text file itself is signed with the GPG key 0xE977A6E990102402 available from keyservers.</p> <p>Please read the README files in each package for more details and instructions.</p> <p><strong>Release notes for version 3.1</strong></p> <p>Version 3.1 is a minor revision with the addition of some alternative input parameter distributions.</p> <p><strong>Release notes for version 3</strong></p> <p>This is the 3rd version representing a re-written analysis of the distance-duality test. This new version updated and renamed the complementary parameter (CP) sets to match the ones used in the paper. New results concerning the interpretation of results as a diagnostics of distance measurement systematics are presented. Also included are updated utility scripts, new tests for Gaussian approximation to the results, and new data-visualization scripts.</p> <p><strong>Earlier versions</strong></p> <p>Earlier versions are available from Zenodo. Links: <a href="https://doi.org/10.5281/zenodo.49825">v1</a>, <a href="https://doi.org/10.5281/zenodo.57982">v2</a>.</p>
DEMAND: a collection of multi-channel recordings of acoustic noise in diverse environments
<p><strong>DEMAND: Diverse Environments Multichannel Acoustic Noise Database</strong></p> <p>A database of 16-channel environmental noise recordings</p> <p><strong>Introduction</strong></p> <p>Microphone arrays, a (typically regular) arrangement of several microphones, allow for a number of interesting signal processing techniques. The correlation of audio signals from microphones that are located in close proximity with each other can, for example, be used to determine the spatial location of sound source relative to the array, or to isolate or enhance a signal based on the direction from which the sound reaches the array.</p> <p>Typically, experiments with microphone arrays that consider acoustic background noise use controlled environments or simulated environments. Such artificial setups will in general be sparse in terms of noise sources. Other pre-existing real-world noise databases (e.g. the <a href="http://catalog.elra.info/product_info.php?products_id=693">AURORA-2</a> corpus, the <a href="http://spandh.dcs.shef.ac.uk/projects/chime/PCC/datasets.html">CHiME</a> background noise data, or the <a href="http://www.speech.cs.cmu.edu/comp.speech/Section1/Data/noisex.html">NOISEX-92</a> database) tend to provide only a very limited variety of environments and are limited to at most 2 channels.</p> <p>The DEMAND (Diverse Environments Multichannel Acoustic Noise Database) presented here provides a set of recordings that allow testing of algorithms using real-world noise in a variety of settings. This version provides 15 recordings. All recordings are made with a 16-channel array, with the smallest distance between microphones being 5 cm and the largest being 21.8 cm.</p> <p><strong>License</strong></p> <p>This work, the audio data and the document describing it, is licensed under a <a href="http://creativecommons.org/licenses/by-sa/3.0/deed.en_CA">Creative Commons Attribution-ShareAlike 3.0 Unported License</a>.</p> <p><strong>The data</strong></p> <p>A description of the data and the recording equipment is provided in the file <strong>DEMAND.pdf</strong>. All recordings are available as 16 single-channel WAV files in one directory at both 48 kHz and 16 kHz sampling rates. All files are compressed into "zip" files.</p> <p><strong>Other information</strong></p> <p>The MATLAB scripts listed in the documentation can be found in the file <strong>scripts.zip</strong>.</p> <p><strong>The Authors</strong></p> <p>This work was created by Joachim Thiemann (IRISA-CNRS), Nobutaka Ito (University of Tokyo), and Emmanuel Vincent (Inria Rennes - Bretagne Atlantique). It was supported by Inria under the Associate Team Program <a href="http://versamus.inria.fr">VERSAMUS</a>.</p>
Room acoustics model of a listening room
<p>This dataset contains a room acoustics model of the IEC listening room at the Technical University of Denmark. The model was generated using the room acoustics software ODEON 13.04 (www.odeon.dk). The acoustical properties of the surfaces were optimized using the ODEON genetic material optimizer after applying initial guesses and measuring impulse responses at multiple source/receiver locations.</p> <p>The dataset contains all files that are needed to run room acoustic simulations in ODEON. For the result files, please contact the author.</p>
DCASE 2018, Task 5: Monitoring of domestic activities based on multi-channel acoustics - Development dataset
<p>This repository contains the development data of task 5 of the DCASE 2018 challenge. The dataset is a derivative of the SINS database.</p> <p>The SINS database contains a continuous recording of one person living in a vacation home over a period of one week. The recordings were manually annotated on daily activity level: "Cooking", "Dishwashing", "Eating", "Social activity (visit, phone call)", "Vacuum cleaning", "Watching TV", "Working", "Presence" and "Absence". More information can be found on (please cite this papers when using the dataset):</p> <p>G. Dekkers, S. Lauwereins, B. Thoen, M. W. Adhana, H. Brouckxon, T. van Waterschoot, B. Vanrumste, M. Verhelst, and P. Karsmakers, “The SINS database for detection of daily activities in a home environment using an acoustic<br> sensor network,” in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), Munich, Germany, November 2017, pp. 32–36.</p> <p>G. Dekkers, L. Vuegen, T. van Waterschoot, B. Vanrumste, and P. Karsmakers, “DCASE 2018 Challenge - Task 5: Monitoring of domestic activities based on multi-channel acoustics,” KU Leuven, Tech. Rep., July 2018.</p> <p>The derivative of the SINS database, 'DCASE 2018 – Task 5 development dataset' consists of data collected by 4 microphone arrays in the combined living room and kitchen area. The continuous recordings were split into audio segments of 10s. These audio segments are provided as individual files along with the ground truth. In total 72984 segments are made available, leading to approximately 200 hours of data.</p> <p>More information about the challenge and the specific dataset can be found <a href="http://dcase.community/challenge2018/task-monitoring-domestic-activities">here</a>. Information solely related to the content of the dataset is available in 'DCASE2018-task5-dev.doc.zip'. <br> <br> <strong>By accessing or using this database, the user accepts the provided EULA (available in DCASE2018-task5-dev.doc.zip).</strong></p>
Data for "Sounding out Ecoacoustic Metrics: Avian species richness is predicted by acoustic indices in temperate but not tropical habitats"
<p>This deposit contains the data for the paper <strong>A Multi-habitat, Comparative Evaluation of Ecoacoustic Indices for Biodiversity Monitoring: Acoustic Indices Predict Avian Species Richness in Temperate but not Tropical Habitats. (Ecological Indicators) </strong>The dataset contains a series of 1 min wav files recorded across UK and Ecuadorian habitats. Each one has 26 acoustic indices calculated on it, and a full list of avian species and abundances and GPS data for each sample site.</p> <p>Abstract</p> <p>Affordable, autonomous recording devices facilitate large scale acoustic monitoring and Rapid Acoustic Survey is emerging as a cost-effective approach to ecological monitoring; the success of the approach rests on the development of computational methods by which biodiversity metrics can be automatically derived from remotely collected audio data. Dozens of indices have been proposed to date, but systematic validation against classical, in situ diversity measures. This study conducted the most comprehensive comparative evaluation to date of the relationship between avian species diversity and a suite of acoustic indices across a wide range of ecological conditions. Acoustic surveys were carried out across habitat gradients in temperate and tropical biomes. Baseline avian species richness and subjective multi-taxa biophonic density estimates were established through aural counting by expert ornithologists. 26 acoustic indices were calculated and compared to observed variations in species diversity. Five acoustic diversity indices (Bioacoustic Index, Acoustic Diversity Index, Acoustic Evenness Index, Acoustic Entropy, and the Normalised Difference Sound Index) were assessed as well as three simple acoustic descriptors (root-mean-square, spectral centroid and zero-crossing rate). Highly significant correlations, of up to 65%, between acoustic indices and avian species richness were observed across temperate habitats, supporting the use of automated acoustic indices in biodiversity monitoring where a single vocal taxon dominates. Significant, weaker correlations were observed in neotropical habitats which host multiple non-avian vocalizing species. Multivariate classification analyses suggest that AIs also track observed differences in habitat-dependent community composition and that each habitat has a distinct soundscape. Multivariate analyses of the relative predictive power of AIs show that compound indices are more powerful predictors of avian species richness than any single index and simple descriptors contribute to predicting avian diversity in multi-taxa tropical environments. Our results support the use of community level acoustic indices as a proxy for species richness and point to the potential for tracking of habitat-dependent changes in community composition. Recommendations for the design of compound indices for multi-taxa community composition appraisal are put forward, with consideration for the requirements of next generation, low power remote monitoring networks.</p> <p> </p> <p><strong>Sampling Methods (extract from paper)</strong></p> <p>Acoustic surveys were carried out along a gradient of habitat degradation (1 forested, 2 regenerating forest and 3 agricultural land) in South East (SE) England and North Western (NW) Ecuador. The six sites (UK1, UK2, UK3, EC1, EC2, EC3) were sampled consecutively from May 6th - Aug 25th 2015.</p> <p>All UK sites were in the county of Sussex, in SE England, an area of weald clays (Fig. 2, left) and included ancient woodland (UK1), regenerating farmland with patches of woodland (UK2) and a downland barley farm (UK3).1 min mono audio recordings made every 15 minutes at three different habitats in the UK</p> <p>Ten day acoustic surveys were carried out consecutively at each study site using 15 Wildlife Acoustics Song Meter audio field recorders. Sampling points were arranged in a grid at a minimum distance of 200 m to minimise pseudo replication (the sound of most species being attenuated over this distance in all biomes). Altitudinal range of sample points across sites was minimised in order to prevent introduction of extraneous, confounding gradients (UK varied between 10 m – 50 m and Ecuador 130 m – 390 m). Recording schedules captured 1 min every 15 min around the clock for 10 days at each site, resulting in 960 recordings at each of 15 sample points for 3 habitat types in 2 different climates (86,400 1 minute recordings in total). Data across the 15 sample points was pooled; inter-site variation was not explored in the current analyses. In the UK 3½ hours of each dawn chorus was sampled starting at 1 hour before sunrise. This range was determined to capture the onset, progression and peak of the dawn chorus, creating a temporal gradient. The equatorial dawn chorus is more compact and was sampled for 2¼ hours starting 15 mins before sunrise, capturing a comparable chorus onset and peak.</p> <p> </p> <p> </p>
Data for "Nonlinear Trapping Stiffness of Mid-Air Single-Axis Acoustic Levitators"
<p>Data associated with the manuscript entitled "Nonlinear Trapping Stiffness of Mid-Air Single-Axis Acoustic Levitators".</p>
TUT Acoustic Scenes 2017 Features
<p>TUT Acoustic Scenes features dataset consists of feature matrices extracted for 10-seconds audio segments from 15 acoustic scenes: </p> <ul> <li>Bus - traveling by bus in the city (vehicle)</li> <li>Cafe / Restaurant - small cafe/restaurant (indoor)</li> <li>Car - driving or traveling as a passenger, in the city (vehicle)</li> <li>City center (outdoor)</li> <li>Forest path (outdoor)</li> <li>Grocery store - medium size grocery store (indoor)</li> <li>Home (indoor)</li> <li>Lakeside beach (outdoor)</li> <li>Library (indoor)</li> <li>Metro station (indoor)</li> <li>Office - multiple persons, typical work day (indoor)</li> <li>Residential area (outdoor)</li> <li>Train (traveling, vehicle)</li> <li>Tram (traveling, vehicle)</li> <li>Urban park (outdoor)</li> </ul> <p>Dataset is split into **Train set** and **Test set**. Each acoustic scene in the Train set has 300 segments, and Test set has 100 segments. The dataset contains similar material to other TUT Acoustic Scenes 2017 datasets: TUT Acoustic Scenes 2017 development dataset and TUT Acoustic Scenes 2017 evaluation dataset. All these datasets are composed from same pool of original audio recordings, but exact audio segments selected for the datasets differ. </p>
Research data supporting "Engineering anisotropic muscle tissue using acoustic cell patterning"
<p>Raw research data supporting the publication:</p> <p>Armstron, JPK et al., "Engineering anisotropic muscle tissue using acoustic cell paterning", Advanced Materials, DOI: 10.1002/adma.201802649 (2018)</p>
Datasets for automatic acoustic identification of individual birds
<p>Bird individual audio recordings (foreground and background) to accompany the work:</p> <p><em><strong>"Automatic acoustic identification of individuals: Improving generalisation across species and recording conditions"</strong></em><br> by Dan Stowell, Tereza Petrusková, Martin Šálek, Pavel Linhart</p> <p><a href="https://royalsocietypublishing.org/doi/10.1098/rsif.2018.0940">https://royalsocietypublishing.org/doi/10.1098/rsif.2018.0940</a></p> <p><br> This dataset contains labelled recordings of individuals from three different bird species:</p> <ul> <li>Little owl</li> <li>Chiffchaff</li> <li>Tree Pipit</li> </ul> <p>For more information, please see the README.txt file, and the research article.</p> <p>The dataset takes approx 11 GB of disk space after the ZIP files have been uncompressed.</p> <p> </p>
SINS database - Node 4 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 7 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 6 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
SINS database - Node 12 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://github.com/KULeuvenADVISE/SINS_database">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
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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.