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1,524 results for “Acoustics”
Fig. 3 in Acoustic Signalling In Eurasian Penduline Tits Remiz Pendulinus: Repertoire Size Signals Male Nest Defence
Fig. 3. Approachdistance (a) and % behaviouralresponses (b) towardsanintruderinre- lationtotheresidentmale'sownrepertoiresize. Behaviouralresponsesincludedcalling, singing, tailquiveringandattacking. Opencirclesindicateresponsesofchallengedresi- dentsonsmallrepertoireplayback, whereasfilledcirclesindicatethesamemales' respons- esonlargerepertoireplayback. Notethatpointsshownontheupperhalfregionof (a) represent males that were mostly present very close to their nest (15 m from the stimulus,
Fig. 3 in Acoustic Discrimination Of Pipistrellus Kuhlii And Pipistrellus Nathusii (Chiroptera: Vespertilionidae) And Its Application To Assess Changes In Species Distribution
Fig. 3. Bar graph about the number of settlements where P. kuhlii and P. nathusii occurred or were absent in case of the two studied areas, from North and South Hungary
Fig. 2 in Acoustic Discrimination Of Pipistrellus Kuhlii And Pipistrellus Nathusii (Chiroptera: Vespertilionidae) And Its Application To Assess Changes In Species Distribution
Fig. 2. Occurrences of the two species in the two studied areas from North and South Hungary. (open circle = none of the species found, black square = P. nathusii, black triangle = P. kuhlii, black circle =
Fig. 1 in Acoustic Discrimination Of Pipistrellus Kuhlii And Pipistrellus Nathusii (Chiroptera: Vespertilionidae) And Its Application To Assess Changes In Species Distribution
Fig. 1. The distribution of the canonical scores between P. kuhlii and P. nathusii resulted from the discriminant function analysis based on 5 call parameters
Atrial Fibrillation Designation with Micro-Raman Spectroscopy and Scanning Acoustic Microscopy
<p>This repository was constructed tp provide the <strong>Raman Spectroscopy</strong> data and figure files related to the manuscript “Atrial Fibrillation Designation with Micro-Raman Spectroscopy and Scanning Acoustic Microscopy”. </p>
Acoustic lures increase tropical forest understorey bat captures
<p>Data used in the publication "Effectiveness of acoustic lures for increasing tropical forest understorey bat captures".</p>
Experimental data on the effects of an azimuthal mean flow on the (thermo)acoustic modes in the annular electroacoustic feedback setup at TU Berlin
<p>Experimental data obtained in the presence of an azimuthal mean flow on the acoustic/thermoacoustic response in the annular electroacoustic feedback setup at TU Berlin. This dataset was used for the published article<br> S. C. Humbert, J. P. Moeck, A. Orchini, C. O. Paschereit, "Effect of an Azimuthal Mean Flow on the Structure and Stability of Thermoacoustic Modes in an Annular Combustor Model With Electroacoustic Feedback", J. Eng. Gas Turbines Power. June 2021, 143(6): 061026. Experimental data as well as Matlab scripts to use them are provided. Useful information is contained in "readme" files.</p>
Oral cancer speech corpus for the paper "Objective speech outcomes after surgical treatment for oral cancer: An acoustic analysis of a spontaneous speech corpus containing 32.850 tokens"
<p>Dataset accompanying the paper "<em>Objective speech outcomes after surgical treatment for oral cancer: An acoustic analysis of a spontaneous speech corpus containing 32.850 tokens</em>"</p> <p>The zip file contains five folders:</p> <p>- <strong>Database:</strong> contains csv files for each speaker which contain the processed features</p> <p>- <strong>Recordings: </strong>the original recording from the YouTube Oral Cancer speech dataset, without further preprocessing</p> <p>- <strong>Recordings_Normalised:</strong> same as recordings but after minimal audio preprocessing (min-max scaling)</p> <p>- <strong>Textgrids: </strong>contains the textgrids which are annotated on the word-level and on phoneme-level</p> <p>- <strong>TIMIT selection: </strong>contains the textgrids for the TIMIT speakers. We unfortunately cannot share the audio date as it is not open source. More information can be found <a href="https://catalog.ldc.upenn.edu/LDC93s1">here.</a></p>
Nocturnal flight calls dataset: long-term acoustic monitoring of birds migrating at night
<p><strong>General Description:</strong></p> <p>This is a development set used in the experiments in the Ph.D. thesis: "Nowe metody akustycznej identyfikacji ptaków migrujących nocą" (<em>"Novel methods of acoustic identification of birds migrating at night"</em>) by Hanna Pamula. The project focuses on the detection (and - partially - classification) of passerine birds' calls from long-term audio recordings collected during bird autumn migration between 2016 and 2019. The dataset consists of >56,5 hours of recordings with annotations of nocturnal flight calls of passerine birds migrating along the Baltic Sea coast, Poland.</p> <p> </p> <p><strong>Folder Structure</strong></p> <p>Development_Set_3.1.zip</p> <p>|_Development_Set_3.1/</p> <p> |__Training_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p> |__Validation_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p> |__Testing_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p>Training Set: 86 recordings</p> <p>Validation Set: 8 recordings</p> <p>Testing set: 18 recordings (BUT: uploaded 20 recordings, as in the previous version of the dataset - version 3, two additional recordings were used. Then, they were deleted in the final version of development set 3.1. Two additional recordings are: 'BUK5_20161101_002104a and BUK5_20161101_002104b)</p> <p>Names of waveforms and annotations are matching.</p> <p><strong>Waveforms:</strong></p> <p>The whole dataset consists of 114 recordings. One hundred thirteen recordings are about 30 minutes long (29min56s – 29min 59s), one recording is 1min20s. All data were recorded at 44,100 Hz sampling rate, one channel, with SM2 Wildlife Acoustics recorders + SMX-NFC microphone. The recording sessions were performed at night (starting time and date denoted in a file name) on the Baltic Sea coast in Poland (Dąbkowice, near Darłowo).</p> <p><strong>Annotations:</strong></p> <p>Transcriptions were produced using Audacity 2.4.1: https://www.audacityteam.org/ by an experienced birdwatcher, Hanna Pamula. While every effort has been made to ensure the quality and accuracy of the labels, some errors may occur, taking into account the difficulty of nocturnal call recognition and transcription tasks in general.</p> <p>Transcription format:</p> <p>[Starting time (sec)] [Ending time (sec)] [Label]</p> <p><strong>Meaning of the labels:</strong></p> <p>1. Positive classes – migrating passerine birds:</p> <ul> <li>'s' – song thrush call (Turdus philomelos)</li> <li>'k' – blackbird call (Turdus merula)</li> <li>'d' – redwing call (Turdus iliacus)</li> <li>'r' – robin call (Erithacus rubecula)</li> <li>‘kwiczol’ – fieldfare call (Turdus pilaris)</li> <li>‘skowronek’ – skylark call (Alauda arvensis)</li> <li>Each of the above labels could also have a question mark '?', e.g. 'r?', 'k?' – meaning that it's not a sure label. In a bird call detection task, they are regarded as positive chunks containing bird call(s).</li> <li>'ni' – non identified bird call (distant/quiet/not recognized)</li> </ul> <p>Only the supposed calls of migrating passerine birds were labeled; other sounds of species were ignored (e.g., robin's tik-calling, which can be often heard at dusk, and may be regarded as warning sounds).</p> <p>2. Negative classes – other marked sound events:</p> <ul> <li>'g' – other bird calls/songs/sounds. Sounds that could confuse the model; for example, sounds of migrating geese, cranes, plovers calls, etc.</li> <li>'gh' – human voices</li> <li>'t' – cracks, clicks, raindrops, other noise</li> <li>‘puszczyk’ – tawny owl voice (Strix aluco)</li> <li>'czapla' – grey heron voice (Ardea cinerea)</li> </ul> <p>Not all occurrences of the negative sounds were labeled – only some chosen examples to represent the possible noises/negative samples. Thus these annotations can't be used for entirely different detection / classification tasks than intended, e.g., detecting migrating cranes or human voices in long-term recordings.</p> <p>3. Labels to be excluded from analysis:</p> <ul> <li>'???', '??? mysz', '??? high freq' – unknown, not sure if the sound event is a birds' call or not. Uncertainty about belonging to a positive/negative class in the detection task.</li> </ul>
Dataset, statistical analysis code, and supplementary material of juvenile ravens' responses towards acoustic cues of different social categories
<p>Social competence i.e., defined as the ability to adjust the expression of social behaviour to the available social information, is known to be influenced by early-life conditions. Brood size might be one of the factors determining such early conditions, particularly in species with extended parental care. We here tested in ravens, whether growing up in families of different sizes affects the chicks' responsiveness to social information. We experimentally manipulated the brood size of 20 captive raven families, creating either small or large families. Simulating dispersal, juveniles were separated from their parents and temporarily housed in one of two captive non-breeder groups. After five weeks of socialization, each raven was individually tested in a playback setting with food-associated calls from three social categories: sibling, familiar unrelated raven they were housed with, and unfamiliar unrelated raven from the other non-breeder aviary. We found that individuals reared in small families were more attentive than birds from large families, in particular towards the familiar unrelated peer. These results indicate that variation in family size during upbringing can affect how juvenile ravens value social information. Whether the observed attention patterns translate into behavioural preferences under daily life conditions remains to be tested in future studies.</p>
Dataset: The effect of hydrogen enrichment, flame-flame interaction, confinement, and asymmetry on the acoustic response of a model can combustor
<p>Complementary dataset for the article: <strong>The effect of hydrogen enrichment, flame-flame interaction, confinement, and asymmetry on the acoustic response of a model can combustor.</strong></p> <p>This document provides a brief description of datasets associated with the paper by Æsøy et al.<br> [1]. The datasets contain flame shapes, flame transfer functions (FTFs), and time series data for<br> perfectly premixed methane/hydrogen/air flames operated in a model can combustor (see [1] for<br> more details). The data is divided into four main categories listed below. Each of these are described<br> in the read-me file in the subsequent sub-folder.</p> <p><strong>• FTF flame interaction:</strong> The level of flame interaction was systematically varied through<br> varying the spacing between flames and by varying the level of hydrogen enrichement of the<br> fuel.<br> <strong>• FTF confinement:</strong> The level of wall confinement was varied by varying the combustion<br> chamber diameter.<br> <strong>• FTF symmetry: </strong>The flame symmetry was varied through equipping the can with different<br> injector geometries.<br> <strong>• Time series:</strong> Time series data of pressure and heat release rate taken with different hydrogen<br> concentrations and combustion chamber lengths.</p> <p>[1] E. Æsøy, T. Indlekofer, F. Gant, A. Cuquel, M. R. Bothien, J. R. Dawson, The effect of hydrogen<br> enrichment, flame-flame interaction, confinement, and asymmetry on the acoustic response of a<br> model can combustor, Combustion and Flame 242 (2022) 112176.</p> <p> </p>
Estimating the abundance of the critically endangered Baltic Proper harbour porpoise (Phocoena phocoena) population using passive acoustic monitoring
<p>Knowing the abundance of a population is a crucial component to assess its conservation status and develop effective conservation plans. For most cetaceans, abundance estimation is difficult given their cryptic and mobile nature, especially when the population is small and has a transnational distribution. In the Baltic Sea, the number of harbour porpoises (<i>Phocoena phocoena</i>) has collapsed since the mid-20<sup>th</sup> century and the Baltic Proper harbour porpoise is listed as Critically Endangered by the IUCN and HELCOM; however, its abundance remains unknown. Here, one of the largest ever passive acoustic monitoring studies was carried out by eight Baltic Sea nations to estimate the abundance of the Baltic Proper harbour porpoise for the first time. By logging porpoise echolocation signals at 298 stations during May 2011-April 2013, calibrating the loggers' spatial detection performance at sea, and measuring the click rate of tagged individuals, we estimated an abundance of 71-1,105 individuals (95% CI, point estimate 491) during May-October within the population's proposed management border. The small abundance estimate strongly supports that the Baltic Proper harbour porpoise is facing an extremely high risk of extinction, and highlights the need for immediate and efficient conservation actions through international cooperation. It also provides a starting point in monitoring the trend of the population abundance to evaluate the effectiveness of management measures and determine its interactions with the larger neighbouring Belt Sea population. Further, we offer evidence that design-based passive acoustic monitoring can generate reliable estimates of the abundance of rare and cryptic animal populations across large spatial scales.</p>
Acoustic models of Brazilian Portuguese Speech based on Neural Transformers - Refinement dataset SPIRA
<p>This dataset was collected over the internet and in hospital wards with the goal of detecting respiratory insufficiency (typically caused by COVID-19). This data collection is part of the SPIRA Project, whose goal is developing a system for recognizing respiratory insufficiency through speech analysis. The datasets presented here were used in the paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers by Marcelo Gauy and Marcelo Finger.</p> <p>The spira_trimmed_data file contains the original ~1 hour dataset collected over the internet (control) and in hospital wards (patients) by the SPIRA Project. This is as described in the paper: Deep learning against COVID-19: Respiratory insufficiency detection in Brazilian Portuguese Speech. We include it here for completeness.</p> <p>The spira_control_full_mp3 file contains the complete ~18 hours control data collected over the internet by the SPIRA project. While not useful for respiratory insufficiency detection, the dataset may be used for identifying age and gender as we mention in our paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers.</p>
Acoustic models of Brazilian Portuguese Speech based on Neural Transformers - Pretraining Datasets raw audios from CORAA
<p>This repository contains all the pretraining datasets used in the paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers by Marcelo Gauy and Marcelo Finger. These datasets are part of a collection of datasets from the TaRSila project (see https://sites.google.com/view/tarsila-c4ai). The audios published here were in part also published with annotations and transcriptions as the CORAA dataset (see https://github.com/nilc-nlp/CORAA). Here we publish the original raw audios from the following datasets (without transcriptions) - ALIP, C-Oral, SP2010, NURC-Recife, NURC-São Paulo and Programa Certas Palavras. In total, the datasets contain about 800 hours of Brazilian Portuguese Speech.</p> <p>The audios have been converted to mp3 to facilitate the upload. ALIP, C-Oral and SP2010 are integrally contained in one file each. Programa Certas Palavras and NURC-Recife are split in 3 parts each, while NURC-SP is split in 7 parts of roughly equal size. More information on the datasets can be found in the paper Acoustic models of Brazilian Portuguese Speech based on Neural Transformers as well as on the original references which created these datasets.</p>
Assessing suspended sediment fluxes with acoustic doppler current profilers: case study from large rivers in Russia
<p>The dataset contains measurements of water discharge by Teledyne RDInstruments RioGrande WorkHorse ADCP unit with a working frequency of 600kHz mounted on a moving boat in 6 areas over large rivers of Russia. The dataset comprises the four largest Arctic Siberian rivers and included continuous ADCP measurements done in 2018-2020 at constant crossection at each river located upper from the impact of recipient seas (tides, surges) near the cities of Salekhard (Ob River), Igarka (Yenisey River), Zhigansk (Lena river) and Chersky (Kolyma River). Another area includes ADCP measurements over 20 transects (named S1…S26, fig. 2) in the lower 200 km of the river Selenga on 27-31July 2018. Additionally, the dataset contains ADCP measurements at 38 points along the Moskva River (named M1, M2…) and 17 tributaries (named T01, T02…) done during 2019-2020.</p> <p>This is a supporting material to a manuscript submitted to «Big Earth Data» journal</p>
Sebastian+Simmons-High-frequency quantitative ultrasound to assess the acoustic properties of engineered tissues in vitro
<p>This dataset includes raw acquired ultrasound data, processing scripts, and statistical data for acoustic property characterization of cell-free and cell-seeded fibrin hydrogels.</p>
Recordings from: Evaluation of a coastal acoustic buoy for cetacean detections, bearing accuracy, and exclusion zone monitoring
<p>1.<span> </span>There is strong socio-political support for offshore wind development in US territorial waters, and construction is planned off several east coast states. Some of the planned development sites coincide with important habitat for critically endangered North Atlantic right whales. Both exclusion zones and passive acoustic monitoring are important tools for managing interactions between marine mammals and human activities. Understanding where animals are with respect to exclusion zones is important to avoid costly construction delays while minimizing the potential for negative impacts. Impact piling from construction of hundreds of offshore wind turbines likely requires exclusion zones as large as 10 km.</p> <p>2.<span> </span>We have developed a three-hydrophone passive acoustic monitoring system that provides bearing information along with marine mammal detections to allow for informed management decisions in real-time. Multiple units form a monitoring system designed to determine whether marine mammal calls originate from inside or outside of an exclusion zone. In October 2021 we undertook a full system validation, with a focus on evaluating the detection range and bearing accuracy of the system with respect to right whale upcalls. Five units were deployed in Mid-Atlantic waters and we played more than >3,500 simulated right whale upcalls at known locations to characterize the detection function and bearing accuracy of each unit. The modeled results of the detection function error were then used to compare the effectiveness of a bearing-based system to a single sensor that can only detect a signal but not ascertain directivity.</p> <p>3.<span> </span>Field trials indicated maximum detection ranges from 4–7.3 km depending on source and ambient noise levels. Simulations showed that incorporating bearing detections provides a substantial improvement in false alarm rates (6 to 12 times depending on number of units, placement, and signal to noise conditions) for a small increase in the risk of missed detections inside of an exclusion zone (1–3%). </p> <p>4.<span> </span>We show that the system can be used for monitoring exclusion zones and clearly highlight the value of including bearing estimation into exclusion zone monitoring plans while noting that placement and configuration of units should reflect anticipated ambient noise conditions.</p>
Acoustic data of calls of Manx shearwater on Lundy Island
<p>Vocalizations are widely used to signal behavioural intention in animal communication, but may also carry additional information encoded in the signal, in particular, vocalisations may carry acoustic signatures unique to the calling individual. Manx shearwater (<em>Puffinus puffinus</em>) are nocturnal seabirds that breed in dense colonies, where they must recognize and locate mates among thousands of conspecifics calling in the dark. There is evidence for individual vocal signatures in two shearwater species, but quantitative data on the vocalisations of Manx shearwater are lacking. We recorded calls of 13 Manx shearwaters on Lundy Island, UK, by eliciting vocal responses to playback of conspecific calls. We measured several spectral and temporal parameters of the calls, applied linear discriminate analysis with leave-one-out cross-validation, and have confirmed the individual vocal signatures. We then calculated among-individual repeatability of 34 features describing the vocalization to determine the extent to which these features may contribute to individual signature coding. We found that calls cluster by individual in both temporal and spectral characteristics, suggesting these are contributing to Manx shearwaters' unique call signatures.</p>
Seismic noise interferometry and Distributed Acoustic Sensing (DAS): Inverting for the firn layer S-velocity structure on Rutford Ice Stream, Antarctica
<p>This dataset contains files including continuous DAS and geophone data and a refracted P wave travel time data collected on Rutford Ice Stream, Antarctica. The seismic data is used to perform seismic noise interferometry. The travel time data is used to perform refraction inversion to get the P wave velocity profile.</p> <p><br> 1. 7 hours of continuous DAS data (100 Hz sampling): 2020-01-14T00:00:19.598000Zoffset_****.mseed, with offset referring to the distance from the DAS channel to the interrogator.</p> <p>2. Corresponding 7 hours of vertical component continuous geophone (A000, located at DAS channel offset 570 m) data.</p> <p>3. Refraction P wave travel time from a geophone array refraction survey.</p>
Non-acoustic speech sensing system based on flexible piezoelectric
<p>The non-acoustic speech sensing system based on flexible piezoelectric is designed to satisfy specific needs around testing device models (in high-noise, complex environments). The system collected vibration signals from the jaws of six males and five females containing ten different control commands at 90 dB of background noise. The dataset is reliable with high intelligibility and is able to achieve 93.7% recognition accuracy by calculation. In general, this paper provides a non-acoustic speech dataset for Mandarin, including the parts collected, the number of people collected, and the environment.</p> <p><br> The dataset is available at:</p> <p>https://doi.org/10.5281/zenodo.7095762</p> <p><br> The data descriptor paper with details of data collection and cleaning process is under submission. For proper citation of the manuscript, please refer to the latest version of this dataset which includes the details.</p> <p>This dataset and its descriptor paper were created by:</p> <p>Shiji Yuan, Ying Sun, Dezhi Zheng, Xinlei Chen,Ying Ding, Shuai Wang, Shangchun Fan</p> <p>For questions or suggestions, please e-mail Dezhi Zheng <zhengdezhi@buaa.edu.cn></p> <p><br> <strong>Description:</strong><br> <br> Ten common words were chosen as the core of the vocabulary in this dataset. These ten command words can be used for commands in IoT or robotics applications: "forward", "backward", "right", "left", "stop", "up", "down", "draw", "drop", and "reset".</p> <p>The recording was carried on by software named Adobe Audition 2022. We set monophonic recording, 16-bit storage format, and 16 kHz sampling frequency before recording and saved the recorded voice in wav format. The dataset is provided with two storage rules, which are stored by subject number and command number as classification. In the first rule, the speech data of 11 subjects were stored in different folders with the subject serial number as the folder name. Each folder contains subfolders categorized by command. In the second rule, the speech data of ten commands are stored in different folders, and the names of the folders are the command contents. </p> <p><br> The subject number, command number and record order are given for each data entry. For example, the data obtained when subject 1 recorded command 10 for the first time was labeled as "1-10_1".</p> <p>After the data collection process, a filtering algorithm for automatic detection of low non-acoustic speech data was designed to remove problematic data that were very short or very quiet.The script of the data filtering algorithm is provided in this repository. </p> <p>For specific detail of the data filtering process, please refer to the script (speech data filtering algorithm in MATLAB) in this repository and the data descriptor paper.</p> <p>The dataset in this repository is the processed version. The raw dataset and removed audio files are not included in this repository.</p> <p><br> <br> <strong>File list:</strong></p> <p><br> Non-acoustic Speech Dataset.zip</p> <p>speech data filtering algorithm.zip</p> <p>Readme.txt </p>
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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.