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14 results for “Spatial audio”
Object-based audio scene files for variations of the spatial arrangement in pop mixes for Wave Field Synthesis
<p>This entry contains object-based audio meta-data to generate the mixes published at http://dx.doi.org/10.5281/zenodo.61000.</p> <p>Have a look at README.md for further details.</p>
Effects of audio-motor training on spatial representations in long-term late blindness
<p>Datasets for behavioural data:</p> <p>-<em>Auditory horizontal localization task</em></p> <p>- <em>Auditory vertical localization task</em></p> <p>- <em>Position matching task</em></p> <p>- <em>Proprioceptive midline task</em></p> <p>Dataset for EEG data:</p> <p>- Spatial bisection task: mean ERP amplitude for 50-90ms timew window for each trial, separately for condition, session and roi</p> <p> </p>
Augmented Objects as Portals into Virtual Worlds: Using Audio to Create Immersive Experiences in Extended Realities - UMBRELLA AUDIO SPATIALIZATION DEMO
<p><strong>Technical demonstration</strong></p> <p>The results of the projection mapping system in the project are clear from the <a href="https://blog.zhdk.ch/immersivearts/dreaming-of-time-and-space/">main documentation video</a>; however, the impact of the spatial audio system in particular, is best experienced from directly underneath the umbrellas, where one can best appreciate the various levels of mixed reality. Unfortunately, it is difficult to document these effects within the artistic context of the project, and as such, we include a brief set of examples to better demonstrate the 6 degree of freedom sound spatialization capabilities of the umbrella system.</p> <p><em><strong>NOTE:</strong></em> The audio in the following examples is recorded from a fixed perspective (initially underneath the umbrella) and rendered binaurally. Unfortunately, the ambisonic microphone used does not capture directionality very well when the source (in this case, the umbrella speakers) is less than ~1 meter away, and in retrospect, a single channel of pink noise was not a wise choice as a source material, as it appears to cause additional phasing issues. Additionally, the effectiveness of binaural audio varies from listener to listener, so <em>the perceived effect in the video is not as strong as when experienced in person</em>; nonetheless, it is possible to get the basic idea of the spatialization algorithm in action from these examples.</p> <p>PLEASE WEAR HEADPHONES IN ORDER TO EXPERIENCE THE 3D EFFECT.</p> <p>In addition to the view of the entire scene from an outside perspective, several other views of the underlying software are displayed throughout the video, including:</p> <ul> <li> <p>A radar view of the scene (umbrella and sound source) as seen by the space manager software, where the:</p> <ul> <li> <p>Blue circle = umbrella</p> </li> <li> <p>Cyan triangle, yellow square = sound source</p> </li> </ul> </li> <li> <p>A view of elements of the spatialization software running on the umbrella, specifically the:</p> <ul> <li> <p>Relative gain calculations and current output levels of each speaker</p> </li> <li> <p>Results of supporting calculations (e.g. sound location after transformation from the global to local coordinate system, and scaling factors used to attenuate the overall volume of the sound as the distance from the umbrella to the sound changes)</p> </li> </ul> </li> </ul> <p><strong>Demo #1</strong></p> <p>Stationary umbrella with a moving virtual sound source (anchored to a rigid body)</p> <p><strong>Demo #2</strong></p> <p>Rotating umbrella with a stationary sound source (anchored to a rigid body)</p> <p><strong>Demo #3</strong></p> <p>Moving umbrella with a fixed sound source (anchored to a rigid body)</p> <p><strong>Demo #4</strong></p> <p>Moving umbrella with a fixed sound source (anchored to a virtual point in space, located above the microphone); as the umbrella approaches the source, the sound first fades into the room, then collapses into the umbrella, as show in Figure 7 ("Fading between umbrella and room with distance") in the main paper</p>
AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio
<p> </p> <p><strong>Abstract</strong></p> <p>We introduce AudioEar3D, a high-quality 3D ear dataset consisting of 112 point cloud ear scans with RGB images, to benchmark the ear reconstruction task. We further collect a 2D ear dataset composed of 2,000 images, each one with manual annotation of occlusion and 55 landmarks, named AudioEar2D. To our knowledge, both datasets have the largest scale and best quality of their kinds for public use.</p> <p>The code is publicly available at <a href="https://medmnist.com/">https://github.com/seanywang0408/AudioEar</a>.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you find this project useful, currently please cite the paper as:</p> <blockquote> <pre>Xiaoyang Huang, Yanjun Wang, Yang Liu, Bingbing Ni, Wenjun Zhang, Jinxian Liu, Teng Li. "AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio". arXiv preprint arXiv:2301.12613, 2023.</pre> </blockquote> <p>or using bibtex:</p> <blockquote> <pre>@article{huang2023audioear, title={AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio}, author={Huang, Xiaoyang and Wang, Yanjun and Liu, Yang and Ni, Bingbing and Zhang Wenjun and Liu Jinxian and Li, Teng}, journal={arXiv preprint arXiv:2301.12613}, year={2023} }</pre> </blockquote> <p> </p> <p><strong>License</strong></p> <p>The dataset is licensed under <em>Creative Commons Attribution 4.0 International</em> (<a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>). </p> <p>The code is under <a href="https://github.com/MedMNIST/MedMNIST/blob/main/LICENSE">Apache-2.0 License</a>.</p> <p> </p> <p><strong>Mirror Link</strong></p> <p>We recommend users to download the data from Zenodo official link. However, if you find any downloading problem, you can also use this mirror link from <a href="https://drive.google.com/drive/folders/1fWTtaFVkEAgLQRz55h8jQt7eJ6omFjxK?usp=sharing">Google Drive</a>.</p> <p> </p> <p><strong>Changelog</strong></p> <p><a href="https://doi.org/10.5281/zenodo.7592895">v1.1</a>: Add README.md.</p> <p><a href="https://doi.org/10.5281/zenodo.7581758">v1.0</a>: Initial repository of AudioEar3D and AudioEar2D.</p> <p> </p>
Dataset for the study "Changes in audio-spatial working memory abilities during childhood: The role of spatial and phonological development"
<p>Working memory is a cognitive system devoted to storage and retrieval processing of information.<br> Numerous studies on the development of working memory have investigated the<br> processing of visuo-spatial and verbal non-spatialized information; however, little is known<br> regarding the refinement of acoustic spatial and memory abilities across development.<br> Here, we hypothesize that audio-spatial memory skills improve over development, due to<br> strengthening spatial and cognitive skills such as semantic elaboration. We asked children<br> aged 6 to 11 years old (n = 55) to pair spatialized animal calls with the corresponding animal<br> spoken name. Spatialized sounds were emitted from an audio-haptic device, haptically<br> explored by children with the dominant hand’s index finger. Children younger than 8<br> anchored their exploration strategy on previously discovered sounds instead of holding this<br> information in working memory and performed worse than older peers when asked to pair<br> the spoken word with the corresponding animal call. In line with our hypothesis, these findings<br> demonstrate that age-related improvements in spatial exploration and verbal coding<br> memorization strategies affect how children learn and memorize items belonging to a complex<br> acoustic spatial layout. Similar to vision, audio-spatial memory abilities strongly depend<br> on cognitive development in early years of life.</p> <p>Data in the file are divided into six sheets based on the age of the participants and the experimental condition, either call-call or call-name. Each sheet contains six columns: Participant ID, age and gender are the first three. The last three columns instead refer to the test parameters: the number of attempts, the audio-anchor and the score. In details, the number of attempts indicates the number of trials needed to pair the sounds. The audio-anchor provides a measurement of the exploration strategy. It accounts for how many consecutive attempts the child begins by touching the same speaker while the score takes into account the frequency of touches on the same speakers: the more the participant returns on the same stimulus location, the lower the score.</p>
Data from: Deciphering complex coral reef soundscapes with spatial audio and 360° video
Open the record for dataset details and reuse information.
A Spatial Audio Impulse Response Compilation Captured at the WDR Broadcast Studios
<p>[1] P. Stade, B. Bernschütz, and M. Rühl, “A Spatial Audio Impulse Response Compilation Captured at the WDR Broadcast Studios,” in <em>Proceedings of the 27th Tonmeistertagung - VDT International Convention</em>, 2012, pp. 1–17.<br> Download link: <a href="http://audiogroup.web.th-koeln.de/FILES/VDT2012_WDRIRC.pdf">Conference Paper TMT2012, Cologne (Germany)</a></p> <p>[2] B. Bernschütz, “Sound Field Analysis in Room Acoustics,” in <em>Proceedings of the 27th Tonmeistertagung - VDT International Convention</em>, 2012, pp. 1–22.<br> Download link: <a href="http://audiogroup.web.th-koeln.de/FILES/VDT2012_SFARA.pdf">Conference Paper TMT2012, Cologne (Germany)</a></p> <p>Files also available at <a href="https://www.sofaconventions.org/mediawiki/index.php/Files">sofaconventions.org</a> in SOFA file format.</p> <p>_______________________________________________________________________________________________________</p> <p>Binaural room impulse responses (BRIRs) and spherical microphone array impulse responses (DRIRs) of the WDR broadcast studios in Cologne in SOFA file format. The BRIRs were measured with a Neumann KU100 dummy head for 360 directions along the horizontal plane (1° spatial resolution). The DRIRs were measured for rigid- and open-sphere configurations on different Lebedev grids. For further details, please refer to the conference publications listed below:</p> <p>________________________________________________________________________________________________________</p> <p><strong>Contact:</strong><br> Christoph Pörschmann<br> TH Köln - University of Applied Sciences<br> Institute of Communications Engineering<br> Department of Acoustics and Audio Signal Processing<br> Betzdorfer Str. 2, D-50679 Cologne, Germany<br> <a href="https://www.th-koeln.de/akustik">https://www.th-koeln.de/akustik</a></p> <p>_________________________________________________________________</p> <p><strong>Naming Convention (please see name_coding_chart.pdf for more details):</strong></p> <p><strong>LBS</strong>: Large Broadcast Studio (Großer Sendesaal / Klaus-von-Bismarck-Saal)</p> <p><strong>SBS:</strong> Small Broadcast Studio (Kleiner Sendesaal)</p> <p><strong>CR1: </strong>Control Room 1</p> <p><strong>CR7</strong>: Control Room 7</p> <p><strong>KU: </strong>Neumann KU100 Dummy Head</p> <p><strong>VSA</strong>: VariSphear Microphone Array</p> <p><strong>MIC</strong>: (Stereo) Microphones</p>
HOMULA-RIR: A Room Impulse Response Dataset for Teleconferencing and Spatial Audio Applications Acquired Through Higher-Order Microphones and Uniform Linear Microphone Arrays
<p>In this paper, we present HOMULA-RIR, a dataset of room impulse responses (RIRs) acquired using both higher-order microphones (HOMs) and a uniform linear array (ULA), in order to model a remote attendance teleconferencing scenario. Specifically, measurements were performed in a seminar room, where a 64-microphone ULA was used as a multichannel audio acquisition system in the proximity of the speakers, while HOMs were used to model 25 attendees actually present in the seminar room. The HOMs cover a wide area of the room, making the dataset suitable also for applications of virtual acoustics. Through the measurement of the reverberation time and clarity index, and sample applications such as source localization and separation we demonstrate the effectiveness of the HOMULA-RIR dataset.</p>
Perceptual thresholds of audio-visual spatial coherence for a variety of audio-visual objects
<p>Dataset accompanying the paper "Perceptual thresholds of audio-visual spatial coherence for a variety of audio-visual objects", published at the AES International Conference on Audio for Virtual and Augmented Reality, Remond, WA, USA</p>
Limits of perceived audio-visual spatial coherence as defined by reaction time measurements
<p>Data accompanying the paper "Limits of perceived audio-visual spatial coherence as defined by reaction time measurements" published in Frontiers in Neuroscience, May 2019.</p>
Data to accompany "Evaluation of Spatial Audio Reproduction Methods (Part 2): Analysis of Listener Preference", J. AES, 2016
<p>This work was supported by the EPSRC Programme Grant S3A: Future Spatial Audio for an Immersive Listener Experience at Home (EP/L000539/1). Details about the data underlying this work, along with the terms for data access, are available from http://dx.doi.org/10.15126/surreydata.00809533</p> <p>If you use the data, please cite the following paper:</p> <p>J. Francombe, T. Brookes, and R. Mason, 2016: Evaluation of Spatial Audio Reproduction Methods (Part 2): Analysis of Listener Preference. Journal of the Audio Engineering Society</p>
Database for Automatic Spatial Audio Scene Classification in Binaural Recordings of Music
<p>This repository contains supplementary material for the paper titled ‘<em>Automatic Spatial Audio Scene Classification in Binaural Recordings of Music</em>.’</p> <p>The database consists of the five following folders:</p> <ol> <li>Binaural recordings used for training</li> <li>Binaural recordings used for testing</li> <li>Extracted features</li> <li>Classification algorithm</li> <li>Music credits</li> </ol>
Perceptually-motivated spatial audio codec for higher-order Ambisonics compression - Examples
<p>Scene-based spatial audio formats, such as Ambisonics, are playback system agnostic and may therefore be favoured for delivering immersive audio experiences to a wide-range of (potentially unknown) devices. The number of channels required to deliver high spatial resolution Ambisonic audio, however, can be prohibitive for low-bandwidth applications. Therefore, in this paper, a compression codec is proposed, which is based upon the higher-order Directional Audio Coding (HO-DirAC) model. The encoder downmixes the higher-order Ambisonics (HOA) input audio into a reduced number of signals, which are accompanied by spatial parameterization metadata. The downmixed audio is coded using a perceptual audio coder, whereas the metadata is grouped into perceptual bands, quantised, and downsampled. On the decoder side, low Ambisonic orders are fully recovered. Whereas, not fully recoverable high Ambisonic orders are synthesized based on the spatial metadata. The results of a listening test indicate that the proposed parametric spatial audio codec can improve the adopted perceptual coder, especially at low to medium-high bitrates, when applied to fifth-order HOA signals.</p>
ARC Code TI: SLAB Spatial Audio Renderer
SLAB is a software-based, real-time virtual acoustic environment rendering system being developed as a tool for the study of spatial hearing. SLAB is designed to work in the personal computer environment to take advantage of the low-cost PC platform while providing a flexible, maintainable, and extensible architecture to enable the quick development of experiments. The software provides an API (Application Programming Interface) for specifying the acoustic scene as well as an extensible architecture for exploring multiple rendering strategies.
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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.