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37 results for “acoustical environment”
Data from: Large-scale manipulation of the acoustic environment can alter the abundance of breeding birds: evidence from a phantom natural gas field
1. Altered animal distributions are a consequence of human expansion and development. Anthropogenic noise can be an important predictor of abundance declines near human infrastructure, yet more information is needed to understand noise impacts at the spatial and temporal scales necessary to alter populations. 2. Energy development and associated anthropogenic noise are globally pervasive, and expanding. For example, 600,000 new natural gas wells have been drilled across central North America in less than twenty years. 3. We experimentally broadcast energy sector noise (recordings of compressor engines) in Southwest Idaho (USA). We placed arrays of speakers creating a "phantom natural gas field" in a large-scale experiment, and tested the effects of noise alone on breeding songbird abundance. To examine variation in human-caused noise, we broadcast two types of compressor noise, one with a slightly higher sound intensity and greater bandwidth than the other. 4. Our phantom natural gas field encompassed approximately 100 km2. We broadcast noise for over three continuous months, for each of two seasons, and quantified over 20,000 hours of background sound levels. 5. Brewer's sparrows (Spizella breweri) were affected by our narrowband playback, declining 30% 50 m from the speaker arrays. During our broadband playback, all species combined and Brewer's sparrows decreased 20% and 33% respectively at the scale of our sites (~0.5 km2; up to 400 m from speaker arrays). 6. Our results show the importance of incorporating the acoustic structure of noise when estimating the cost of noise exposure for populations and suggest an urgent need for noise mitigation, such as quieting compressor station noise, in energy extraction fields and natural areas broadly.
SINS database - Node 1 - 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://kuleuvenadvise.github.io/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>
Binaural detection thresholds and audio quality of speech and music signals in complex acoustic environments
<p>Every-day acoustical environments are often complex, typically comprising one attended target sound in the presence of interfering sounds (e.g., disturbing conversations) and reverberation. Here we assessed binaural detection thresholds and (supra-threshold) binaural audio quality ratings of four distortions types: spectral ripples, non-linear saturation, intensity and spatial modifications applied to speech, guitar, and noise targets in such complex acoustic environments (CAEs). The target and (up to) two masker sounds were either co-located as if contained in a common audio stream, or were spatially separated as if originating from different sound sources. The amount of reverberation was systematically varied. Masker and reverberation had a significant effect on the distortion-detection thresholds of speech signals. Quality ratings were affected by reverberation, whereas the effect of maskers depended on the distortion. The results suggest that detection thresholds and quality ratings for distorted speech in anechoic conditions are also valid for rooms with mild reverberation, but not for moderate reverberation. Furthermore, for spectral ripples, a significant relationship between the listeners’ individual detection thresholds and quality ratings was found. The current results provide baseline data for detection thresholds and audio quality ratings of different distortions of a target sound in CAEs, supporting the future development of binaural auditory models.</p>
Virtual acoustic street environment
<p><strong>Virtual acoustic street environment</strong></p> <p>This dataset contains files needed to render a virtual acoustic street environment in TASCAR (version 0.228 or newer). See Street_Environment_Description.pdf for details.</p> <p><strong>Authors:</strong></p> <p>Giso Grimm (session file)</p> <p>This file can be used and distributed according to following license conditions:</p> <p><strong>Licenses:</strong></p> <p><a href="https://creativecommons.org/licenses/by/3.0/">CC BY 3.0</a> (76804__audible-edge__ae0090-volvo-740-gle-handbrake-turn-01.wav)<br> <a href="https://creativecommons.org/licenses/by-nc-sa/3.0/">CC BY-NC-SA 3.0</a> (Story6.flac, baby_talks.wav)<br> <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (coke_can_2wheels_pram.flac)<br> <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> (session file)<br> <a href="https://creativecommons.org/licenses/by-sa/3.0/">CC BY-SA 3.0</a> (la_le_lu.flac, pramwheels.wav, redcar_loop1.wav, whitevan_loop1.wav)<br> <a href="https://creativecommons.org/share-your-work/public-domain/cc0/">CC0</a> (apts.wav, bike_bell.wav, bus.flac, footsteps.wav, martin.wav, train_ax1.wav, train_ax2.wav, train_ax3.wav, train_ax4.wav, train_ax5.wav, train_engine.wav, truckbeep.flac)</p> <p><strong>Attributions:</strong></p> <p>Giso Grimm (baby_talks.wav, pramwheels.wav, redcar_loop1.wav, session file, whitevan_loop1.wav)<br> Maartje Hendrikse (Story6.flac)<br> Sabine Hochmut (la_le_lu.flac)<br> Theda Eichler, Giso Grimm (coke_can_2wheels_pram.flac)<br> audible-edge / freesound.org (76804__audible-edge__ae0090-volvo-740-gle-handbrake-turn-01.wav)</p> <p><strong>Acknowledgements:</strong></p> <p>Thanks to Marie Hartwig for her support in the preparation of this upload.</p> <p><strong>Bibliography:</strong></p> <p>Grimm, Giso; Luberadzka, Joanna; Hohmann, Volker. <em>A Toolbox for Rendering Virtual Acoustic Environments in the Context of Audiology.</em> Acta Acustica united with Acustica, Volume 105, Number 3, May/June 2019, pp. 566-578(13), <a href="https://doi.org/10.3813/AAA.919337">https://doi.org/10.3813/AAA.919337</a></p> <p>Hendrikse, M. M., Llorach, G., Hohmann, V., & Grimm, G. (2019). <em>Movement and gaze behavior in virtual audiovisual listening environments resembling everyday life.</em> Trends in Hearing, 23, <a href="https://doi.org/10.1177/2331216519872362">https://doi.org/10.1177/2331216519872362</a></p> <p>Grimm, Giso, & Hohmann, Volker. (2019). <em>First Order Ambisonics field recordings for use in virtual acoustic environments in the context of audiology.</em> Zenodo. <a href="https://doi.org/10.5281/zenodo.3588303">https://doi.org/10.5281/zenodo.3588303</a></p> <p>Hendrikse, Maartje M. E., Dingemanse, Gertjan, Grimm, Giso, Hohmann, Volker, & Goedegebure, André. (2022, September 19). Virtual audiovisual scenes for hearing device fine-tuning. Zenodo. <a href="https://doi.org/10.5281/zenodo.7092790">https://doi.org/10.5281/zenodo.7092790</a></p>
Data from: Large-scale manipulation of the acoustic environment can alter the abundance of breeding birds: evidence from a phantom natural gas field
Open the record for dataset details and reuse information.
Utility of acoustic indices for ecological monitoring in complex sonic environments
<p>Abstract</p> <p>With the continued adoption of passive acoustic monitoring as a tool for rapid and high-resolution ecosystem monitoring, ecologists are increasingly making use of a suite of acoustic indices to summarise the sonic environment. Though these indices are often reported to well represent some aspect of the biology of an ecosystem, the degree to which they are confounded by various extraneous sonic conditions is largely unknown. We conducted an aural inventory across 23 field sites in Okinawa to identify the number of unique animal sounds present in recordings. Using these values of ‘measured richness’, we then examined how the performance of 11 commonly-used acoustic indices varied across a range of sonic conditions (including in the presence and absence of insect stridulations, audible wind or rain, and human-related sounds). Our analysis identified both well- and poor-performing acoustic indices, as well as those that were particularly sensitive to sonic conditions. Only two indices reflected measured richness across the full range of sonic conditions examined. A few indices were relatively insensitive to extraneous sonic conditions, but no index correlated with measured richness when masked by sound from broadband stridulating insects. Our results demonstrate considerable sensitivity of most commonly used acoustic indices to confounding sonic conditions, highlighting the challenges of working with large acoustic datasets collected in the field. We make practical recommendations for acoustic index use based on study design, with the aim of identifying the suite of acoustic indices with greatest utility as indicators for rapid biodiversity monitoring and management of the world’s natural soundscapes.</p> <p>Methods</p> <p>The dataset contains the names of audio files collected across 23 field sites between April 2017 and January 2018 as part of the OKEON-Churamori project on the island of Okinawa, Japan. We conducted an aural inventory, manually counting and recording the number of unique biotic sounds (approximately corresponding to species richness) and noting the presence or absence of three potentially confounding sonic conditions: audible geophony (wind, rain etc.), anthropophony (human-related sounds), and broadband sounds produced by stridulating insect (e.g. cicadas, orthopterans). We then calculated 11 commonly used acoustic indices from the literature and compared their performance (correlation with richness) in the presence vs absence of each sonic condition. Our dataset also contains time and date information for each recording, and the mean site-level richness (i.e. across multiple recordings) for each site and for each unique site-by-season combination. See Table A2 and Methods section in the associated manuscript for details on data processing and the calculation of acoustic indices.</p> <p>Usage notes</p> <p>See readme file for descriptions of data table structure.</p>
Data files from nonreciprocal acoustics in viscous environment
<p>It is demonstrated that acoustic transmission through a phononic crystal with anisotropic solid scatterers becomes nonreciprocal if the background fluid is viscous. In an ideal (inviscid) fluid, the transmission along the direction of broken P symmetry is asymmetric. This asymmetry is compatible with reciprocity since time-reversal symmetry (T symmetry) holds. Viscous losses break T symmetry, adding a nonreciprocal contribution to the transmission coefficient. The nonreciprocal transmission spectra for a phononic crystal of metallic circular cylinders in water are experimentally obtained and analyzed. The surfaces of the cylinders were specially processed in order to weakly break P symmetry and increase viscous losses through manipulation of surface features. Subsequently, the nonreciprocal part of transmission is separated from its asymmetric reciprocal part in numerically simulated transmission spectra. The level of nonreciprocity is in agreement with the measure of broken P symmetry. The reported study contradicts commonly accepted opinion that linear dissipation cannot be a reason leading to nonreciprocity. It also opens a way for engineering passive acoustic diodes exploring natural viscosity of any fluid as a factor leading to nonreciprocity.</p>
Data from: AudioMoth: evaluation of a smart open acoustic device for monitoring biodiversity and the environment
1. The cost, usability and power efficiency of available wildlife monitoring equipment currently inhibits full ground-level coverage of many natural systems. Developments over the last decade in technology, open science, and the sharing economy promise to bring global access to more versatile and more affordable monitoring tools, to improve coverage for conservation researchers and managers. 2. Here we describe the development and proof-of-concept of a low-cost, small-sized and low-energy acoustic detector: 'AudioMoth'. The device is open-source and programmable, with diverse applications for recording animal calls or human activity at sample rates of up to 384kHz. We briefly outline two ongoing real-world case studies of large-scale, long-term monitoring for biodiversity and exploitation of natural resources. These studies demonstrate the potential for AudioMoth to enable a substantial shift away from passive continuous recording by individual devices, towards smart detection by networks of devices flooding large and inaccessible ecosystems. 3. The case studies demonstrate one of the smart capabilities of AudioMoth, to trigger event logging on the basis of classification algorithms that identify specific acoustic events. An algorithm to trigger recordings of the New Forest cicada (Cicadetta montana) demonstrates the potential for AudioMoth to vastly improve the spatial and temporal coverage of surveys for the presence of cryptic animals. An algorithm for logging gunshot events has potential to identify a shotgun blast in tropical rainforest at distances of up to 500 m, extending to 1km with continuous recording. 4. AudioMoth is more energy efficient than currently available passive acoustic monitoring (PAM) devices, giving it considerably greater portability and longevity in the field with smaller batteries. At a build cost of ~US$43 per unit, AudioMoth has potential for varied applications in large-scale, long-term acoustic surveys. With continuing developments in smart, energy-efficient algorithms and diminishing component costs, we are approaching the milestone of local communities being able to afford to remotely monitor their own natural resources.
Data from: Uncovering spatial variation in acoustic environments using sound mapping
Animals select and use habitats based on environmental features relevant to their ecology and behavior. For animals that use acoustic communication, the sound environment itself may be a critical feature, yet acoustic characteristics are not commonly measured when describing habitats and as a result, how habitats vary acoustically over space and time is poorly known. Such considerations are timely, given worldwide increases in anthropogenic noise combined with rapidly accumulating evidence that noise hampers the ability of animals to detect and interpret natural sounds. Here, we used microphone arrays to record the sound environment in three terrestrial habitats (forest, prairie, and urban) under ambient conditions and during experimental noise introductions. We mapped sound pressure levels (SPLs) over spatial scales relevant to diverse taxa to explore spatial variation in acoustic habitats and to evaluate the number of microphones needed within arrays to capture this variation under both ambient and noisy conditions. Even at small spatial scales and over relatively short time spans, SPLs varied considerably, especially in forest and urban habitats, suggesting that quantifying and mapping acoustic features could improve habitat descriptions. Subset maps based on input from 4, 8, 12 and 16 microphones differed slightly (< 2 dBA/pixel) from those based on full arrays of 24 microphones under ambient conditions across habitats. Map differences were more pronounced with noise introductions, particularly in forests; maps made from only 4-microphones differed more (> 4 dBA/pixel) from full maps than the remaining subset maps, but maps with input from eight microphones resulted in smaller differences. Thus, acoustic environments varied over small spatial scales and variation could be mapped with input from 4–8 microphones. Mapping sound in different environments will improve understanding of acoustic environments and allow us to explore the influence of spatial variation in sound on animal ecology and behavior.
Data Corpus for the IEEE-AASP Challenge on the Acoustic Characterization of Environments (ACE)
<p>The aim of this challenge was to evaluate state-of-the-art algorithms for blind acoustic parameter estimation from speech and to promote the emerging area of research in this field.</p> <p>Several established parameters and metrics have been used to characterize the acoustics of a room. The most important are the Direct-To-Reverberant Ratio (DRR), the Reverberation Time (<em>T60</em>) and the reflection coefficient. The acoustic characteristics of a room based on such parameters can be used to predict the quality and intelligibility of speech signals in that room. Recently, several important methods in speech enhancement and speech recognition have been developed that show an increase in performance compared to the predecessors but do require knowledge of one or more fundamental acoustical parameters such as the <em>T60</em>. Traditionally, these parameters have been estimated using carefully measured Acoustic Impulse Responses (AIRs). However, in most applications it is not practical or even possible to measure the acoustic impulse response. Consequently, there is increasing research activity in the estimation of such parameters directly from speech and audio signals.</p> <p><strong>Documentation and software</strong></p> <ul> <li>Corpus instructions including software operating instructions</li> <li>Software to generate new datasets from the corpus materials (Matlab)</li> <li><em>T</em>60 and DRR measurements in fullband and <a href="http://www.iso.org/iso/catalogue_detail.htm?csnumber=1350">ISO-266</a> preferred frequency bands</li> <li>Room dimensions and approximate positions of microphones and sources</li> </ul> <p><strong>Anechoic speech</strong></p> <p>Comprising Development (Dev): 4 male talkers, 2 utterances each, and Evaluation (Eval): 5 male and 5 female talkers, 5 utterances each, recorded using the anechoic chamber at <a href="http://www.tudelft.nl/en/">TU Delft</a> at <em>fs</em>=48 kHz in 16-bit format. Plain text (.txt) transcriptions of each .wav file are included.</p> <p><strong>RIRs and noise by microphone configuration</strong></p> <p>Each archive below contains the set of <em>fs</em>=48 kHz 16-bit RIRs, ambient, fan and babble noise .wav files for each room and microphone position for that microphone configuration, recorded in 7 different rooms in the <a href="http://www3.imperial.ac.uk/electricalengineering">Dept. of Electrical and Electronic Engineering at Imperial College London</a>.</p> <p>The corpus comprises the following components:</p> <ul> <li>Single-channel (based on cruciform channel 1) 417 MB</li> <li>2-channel laptop 1.05 GB</li> <li>3-channel mobile 1.59 GB</li> <li>5-channel cruciform 2.84 GB</li> <li>8-channel linear 4.24 GB</li> <li>32-channel spherical 14.2 GB</li> </ul> <p>The corpus and the ACE Challenge are described in the following <a href="https://www.researchgate.net/publication/303854321_Estimation_of_room_acoustic_parameters_The_ACE_Challenge">journal paper</a>:</p> <ul> <li>J. Eaton; N. D. Gaubitch; A. H. Moore; P. A. Naylor, "Estimation of room acoustic parameters: The ACE Challenge," in <em><a href="http://ieeexplore.ieee.org/document/7486010/">IEEE/ACM Transactions on Audio, Speech, and Language Processing</a></em>, vol. 24, no.10, pp.1681-1693, Oct. 2016.</li> </ul> <p>Please cite this whenever you use any part of the corpus. BibTeX references are available here for the <a href="http://www.commsp.ee.ic.ac.uk/~sap/uploads/data/ACE/ACE_IEEE_ref.bib">journal paper</a> and <a href="http://www.commsp.ee.ic.ac.uk/~sap/uploads/data/ACE/ACE_Tech_ref.bib">technical report</a>.</p> <ul> </ul>
Evaluation of a Binaural Beamformer (StereoZoom) in a Virtual Acoustic Environment and in Real Life
ClinicalTrials.gov study NCT03361527. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Data from: AudioMoth: evaluation of a smart open acoustic device for monitoring biodiversity and the environment
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Data files from nonreciprocal acoustics in viscous environment
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Data from: Uncovering spatial variation in acoustic environments using sound mapping
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Children across cultures respond emotionally to the acoustic environment
<p>Among human and non-human animals, the ability to respond rapidly to biologically significant events in the environment is essential for survival and development. Research has confirmed that human adult listeners respond emotionally to environmental sounds just as they understand the emotional connotations of speech prosody and music. However, it is unknown whether young children also respond emotionally to environmental sounds. Here, we report that changes in pitch, rate (i.e., playback speed), and intensity (i.e., amplitude) of environmental sounds trigger emotional responses in 4- and 5-year-old children, including sounds of human actions, animal calls, machinery, or natural phenomena such as wind and waves. This phenomenon was observed for children from the United States and China – countries with drastically different cultural traditions. We discuss theoretical frameworks that predict convergent emotional responses to music, speech, and environmental sounds, focusing on Charles Darwin's hypothesis that speech and music originated from a common emotional signal system based on the imitation and modification of environmental sounds.</p>
Children across cultures respond emotionally to the acoustic environment
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Visual and Acoustic Effects of Human Thermal Comfort and Perception in a Micro-Climatically Steady Environment
ClinicalTrials.gov study NCT06985940. IPD Sharing: YES. Countries: 1. Publications: 0.
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.