Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
27
datasets available to search
ShareScore release 0.9.0
Dataset results
27 results for “sound localization”
Binaural room scanning files for sound field synthesis localization experiment
<p>Binaural room scanning files that were used together with the SoundScape Renderer to perform the localization experiments described in Wierstorf [1].</p> <p>The results of the corresponding listening experiments are summarized in Fig. 5.4, see https://github.com/hagenw/phd-thesis/tree/master/05_psychoacoustics/fig5_04</p> <p>[1] H. Wierstorf, Perceptual Assessment of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014.</p>
Listening test results for sound field synthesis localization experiment
<p>Result files from the the localization experiments described in section 5.1 of Wierstorf [1].</p> <p>The results are visually summarized in Fig. 5.4, see https://github.com/hagenw/phd-thesis/tree/master/05_psychoacoustics/fig5_04</p> <p>[1] H. Wierstorf, Perceptual Assessment of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014.</p>
Listening test results for sound field synthesis localization experiment -- head movement data
<p>This data set contains recorded head movements listeners did during several localisation tasks in the context of sound field synthesis. This is an add-on to the actual localisation results provided by [1].</p> <p>[1] Wierstorf, H. (2016). Listening test results for sound field synthesis localization experiment [Data set]. Zenodo. http://doi.org/10.5281/zenodo.55439</p>
[Dataset] Simultaneous laser ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopy
<p>Research data for the purpose of reproducing the results presented in the journal publication titled "Simultaneous laser ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopy"</p>
Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.
<p>supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146</p>
Dataset for sound source localization with 101 Blinky sound-to-light conversion sensors
<p>Blinkies are sound-to-light conversion devices that can be used to monitor the sound level over large areas. The data from the sensors is harvested using a video camera. This dataset contains seven videos that were recorded in the gymnastical hall of Tokyo Metropolitan University, Hino Campus on July 3rd 2018. In the video, 101 Blinkies are spread on the ground of the gymnastic hall. A bluetooth speaker mounted on a remote controlled car runs between the Blinkies, causing them to change intensity. The file `pyramic_json` is a JSON format file containing all the meta-data necessary such as sensor locations, room dimensions, and segmentation information.</p> <p>This dataset was used to demonstrate sound source localization in the paper "Blinkies: Open source sound-to-light conversion sensors for large-scale acoustic sensing and applications" by Robin Scheibler and Nobutaka Ono (to appear).</p>
Supporting Data for Figures in "Localized, tidal energy extraction in Puget Sound can adjust estuary resonance and friction, modifying barotropic tides system-wide"
<p>Supporting data for figures in "Localized, tidal energy extraction in Puget Sound can adjust estuary resonance and friction, modifying barotropic tides system-wide" by Preston S. Spicer, Parker MacCready, and Zhaoqing Yang. The manuscript is being considered for publication in Journal of Geophysical Research: Oceans (2024). The article analyzes the effect of a tidal turbine farm on incident and reflected tidal energy fluxes in the Salish Sea. Files are in MATLAB data and .m format with some .txt and shape files. Files named figX.m create the corresponding Figure X using provided .mat and other files. Variable names and units correspond to graphed data of each figure in the journal article.</p>
Data from: Domain-specific neural networks improve automated bird sound recognition already with small amount of local data
<p><span><span>An automatic bird sound recognition system is a useful tool for collecting data of different bird species for ecological analysis. Together with autonomous recording units (ARUs), such a system provides a possibility to collect bird observations on a scale that no human observer could ever match. During the last decades progress has been made in the field of automatic bird sound recognition, but recognizing bird species from untargeted soundscape recordings remains a challenge. <br></span></span></p> <p><span><span>In this article we demonstrate the workflow for building a global identification model and adjusting it to perform well on the data of autonomous recorders from a specific region. We show how data augmentation and a combination of global and local data can be used to train a convolutional neural network to classify vocalizations of 101 bird species. We construct a model and train it with a global data set to obtain a base model. The base model is then fine-tuned with local data from Southern Finland in order to adapt it to the sound environment of a specific location and tested with two data sets: one originating from the same Southern Finnish region and another originating from a different region in German Alps.<br></span></span></p> <p><span><span>Our results suggest that fine-tuning with local data significantly improves the network performance. Classification accuracy was improved for test recordings from the same area as the local training data (Southern Finland) but not for recordings from a different region (German Alps). Data augmentation enables training with a limited number of training data and even with few local data samples significant improvement over the base model can be achieved. Our model outperforms the current state-of-the-art tool for automatic bird sound classification.<br></span></span></p> <p><span><span>Using local data to adjust the recognition model for the target domain leads to improvement over general non-tailored solutions. The process introduced in this article can be applied to build a fine-tuned bird sound classification model for a specific environment.</span></span></p>
Monaural and binaural sound localization cues in crocodilians
<p>This dataset is composed by all the recorded microphonic signals necessary for the computation of external sound localization cues: HRTFs (Head-Related Transfer Functions), Interaural Level Differences (ILD) and Interaural Time Differences (ITD) on awake crocodilians (<em>Crocodylus niloticus</em> and <em>Caiman latirostris</em>) and skulls(<em>Crocodylus niloticus</em>).</p> <p>The Matlab scripts necessary to compute and display HRTF, ILD and ITD are included as well as instructions in txt and pdf files.</p>
Short-term effects of sound localization training in virtual reality
<p>This repository contains the dataset and software used in the submission to Scientific Reports entitled "Short-term effects of sound localization training in virtual reality".</p> <p>A README.txt file is included, which describes the contents of the repository.</p> <p>Please note that the authors were not granted permission to redistribute the LIMSI Spatialization Engine, which was used to spatialize the sounds described in the manuscript. Please contact the author(s) for details.</p>
Data: "Happy new ears: rapid adaptation to novel spectral cues in vertical sound localization."
<p>Data related to the manuscript entitled: "Happy new ears: rapid adaptation to novel spectral cues in vertical sound localization.". For more information, read the README file.</p>
Data from: Domain-specific neural networks improve automated bird sound recognition already with small amount of local data
Open the record for dataset details and reuse information.
Sound source localization with varying amount of visual information in virtual reality [dataset]
<p>This is the dataset that belongs to the publication "Sound source localization with varying amount of visual information in virtual reality".</p> <p>-The file "AVIL_lab.fbx" contains the visual model of the loudspeaker environment that was used throughout the experiment.</p> <p>-The file "localization_task_instruction_final.docx" contains the information sheet that was handed to the subjects before the experiment.</p> <p>-The file "localizationData_public.xlsx" contains the responses from the subjects (column B). The responses in azimuth and elevation (columns F&G) are corrected for the pointing bias (columns H&I).</p>
Data of Listening Experiments for Azimuthal Localisation in (Local) Sound Field Synthesis
<p>Data of two listening experiments conducted at University of Rostock, Germany. The study investigated the four (Local) Sound Field Synthesis techniques</p> <ul> <li>Wave Field Synthesis</li> <li>Near-Field-Compensated Higher-Order Ambisonics</li> <li>Local Wave Field Synthesis using Spatial Bandwidth Limitation</li> <li>Local Wave Field Synthesis using Virtual Secondary Sources</li> </ul> <p>The corresponding binaural room scanning (BRS) files for the binaural simulation can be found in the directory `brs`. The employed noise stimulus is contained in `stimuli`. The localisation results are stored in `results`. The `analysis` directory includes scripts for parsing the data.</p>
SLoClas: A Database for Joint Sound Localization and Classification
<p>We present a new database namely Sound Localization and Classification (SLoClas) corpus, for studying and analyzing sound localization and classification. The corpus contains a total of 23.27 hours of data recorded using a 4-channel microphone array. 10 classes of sounds are played over a loudspeaker at 1.5 meters distance from the array by varying the DoA from 1 degree to 360 degree at an interval of 5 degree. To facilitate the study of noise robustness, 6 types of outdoor noise are recorded at 4 DoAs, using the same devices.</p> <p>We release this database for research purpose only. If you use this corpus, cite the following paper</p> <p>Qian Xinyuan, Bidisha Sharma, Amine El Abridi, and Haizhou Li. "SLoClas: A Database for Joint Sound Localization and Classification." <em>arXiv preprint arXiv:2108.02539</em> (2021).</p> <p> </p>
Self-organising cicada choruses respond to the local sound and light environment
<p>1. Periodical cicadas exhibit an extraordinary capacity for self-organising spatially synchronous breeding behavior. The regular emergence of periodical cicada broods across the US is a phenomenon of longstanding public and scientific interest, as the cicadas of each brood emerge in huge numbers and briefly dominate their ecosystem. During the emergence, the 17-year periodical cicada species Magicicada cassini is found to form synchronised choruses, and we investigated their chorusing behavior from the standpoint of spatial synchrony.</p> <p>2. Cicada choruses were observed to form in trees, calling regularly every five seconds. In order to determine the limits of this self-organising behaviour, we set out to quantify the spatial synchronisation between cicada call choruses in different trees, and how and why this varies in space and time.</p> <p>3. We performed 20 simultaneous recordings in Clinton State Park, Kansas, in June 2015 (Brood IV) with a team of citizen-science volunteers using consumer equipment (smartphones). We use a wavelet approach to show in detail how spatially synchronous, self-organised chorusing varies across the forest.</p> <p>4. We show how conditions that increase the strength of audio interactions between cicadas also increase the spatial synchrony of their chorusing. Higher forest canopy light levels increase cicada activity, corresponding to faster and higher-amplitude chorus cycling and to greater synchrony of cycles across space. We implemented a relaxation-oscillator-ensemble model of interacting cicadas, finding that a tendency to call more often, driven by light levels, results in all these effects.</p> <p>5. Results demonstrate how the capacity to self-organise in ecology depends sensitively on environmental conditions. Spatially correlated modulation of cycling rate by an external driver can also promote self-organisation of phase synchrony.</p>
Data from: Human sound localization depends on sound intensity: implications for sensory coding
Human sound localization is an important computation performed by the brain. Models of sound localization commonly assume that sound lateralization from interaural time differences is level invariant. Here we observe that two prevalent theories of sound localization make opposing predictions. The labelled-line model encodes location through tuned representations of spatial location and predicts that perceived direction is level invariant. In contrast, the hemispheric-difference model encodes location through spike-rate and predicts that perceived direction becomes medially biased at low sound levels. Here, behavioral experiments find that softer sounds are perceived closer to midline than louder sounds, favoring rate-coding models of human sound localization. Analogously, visual depth perception, which is based on interocular disparity, depends on the contrast of the target. The similar results in hearing and vision suggest that the brain may use a canonical computation of location: encoding perceived location through population spike rate relative to baseline.
Sound event localization and detection (SELDnet) results
<p>This package is part of the work - <a href="https://github.com/sharathadavanne/seld-metric">Joint Measurement of Localization and Detection of Sound Events</a> presented in WASPAA 2019.</p> <p>This package consists of results from the <a href="https://arxiv.org/abs/1905.08546">SELDnet method</a> for joint sound event localization and detection. The results corresponding to different training states of 5, 25 and 75 epochs are provided here. These results are for the four cross-validation splits of the <strong>TAU Spatial Sound Events 2019 - Microphone Array </strong>dataset. The sound events in this dataset consist of stationary point sources from multiple sound classes each associated with a temporal onset and offset time, and DOA coordinate represented using azimuth and elevation angle. This <strong>TAU Spatial Sound Events 2019 - Microphone Array </strong>dataset is part of the <a href="https://github.com/sharathadavanne/seld-dcase2019">DCASE 2019 Sound Event Localization and Detection Task</a> and can be downloaded <a href="https://zenodo.org/record/2599196#.XT_RmHUzaCg">here</a>.</p> <p>Each of the results folders consists of 400 files, corresponding to the results of the individual recordings of the <strong>TAU Spatial Sound Events 2019 - Microphone Array </strong>dataset.</p> <p>This data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND.</p> <p><strong>Download instructions</strong></p> <p>The three files, <strong><em>mic_dev_5</em></strong>,<strong><em> mic_dev_25, </em></strong>and <strong><em>mic_dev_75</em></strong>, correspond to SELDnet results at 5, 25 and 75 epochs.</p> <p>Download the zip files and use your favorite compression tool to unzip these split zip files.</p>
Self-organising cicada choruses respond to the local sound and light environment
Open the record for dataset details and reuse information.
Data from: Human sound localization depends on sound intensity: implications for sensory coding
Open the record for dataset details and reuse information.
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.