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13 results for “acoustic simulations”
Acoustic Data for Endotracheal Intubation Simulation with Machine Learning Feedback
<p>This dataset contains raw acoustic data collected during endotracheal intubation simulations, utilized for developing a machine learning-based performance feedback system. The data includes .wav audio recordings sampled at 192 kHz, organized by buzzer and microphone location and intubation states.</p><p>The data is associated with the following paper:</p><p>Steffensen, T. L., Bartnes, B., Fuglstad, M. L., Auflem, M., & Steinert, M. (2023). Playing the pipes: Acoustic sensing and machine learning for performance feedback during endotracheal intubation simulation. <i>Frontiers in Robotics and AI</i>, <i>10–2023</i>. https://doi.org/10.3389/frobt.2023.1218174</p>
Data and code used in analyses for Simulated soundscapes and transfer learning boost the performance of acoustic classifiers under data scarcity
<p>Evaluation datasets, Python scripts, and computation environments used to conduct analyses for Simulated soundscapes and transfer learning boost the performance of acoustic classifiers under data scarcity. <br><br>transfer_learning_project.zip also contains a vignette describing the use of a generalized script for adapting these methods to novel acoustic classification tasks. </p> <p> </p>
Data for: Collective signalling is shaped by feedbacks between signaller variation, receiver perception, and acoustic environment in a simulated communication network
<p>Communication takes place within a network of multiple signallers and receivers. Social network analysis provides tools to quantify how an individual's social positioning affects group dynamics, and the subsequent biological consequences. However, network analysis is rarely applied to animal communication, likely due to the logistical difficulties of monitoring natural communication networks. We generated a simulated communication network to investigate how variation in individual communication behaviours generates network effects, and how this communication network's structure feeds back to affect future signalling interactions. We simulated competitive acoustic signalling interactions among chorusing individuals and varied several parameters related to communication and chorus size to examine their effects on calling output and social connections. Larger choruses had higher noise levels, and this reduced network density and altered the relationships between individual traits and communication network position. Hearing sensitivity interacted with chorus size to affect both individuals' positions in the network and the acoustic output of the chorus. Physical proximity to competitors influenced signalling, but a distinctive communication network structure emerged when signal active space was limited. Our model raises novel predictions about communication networks that could be tested experimentally, and identifies aspects of information processing in complex environments that remain to be investigated. </p>
Assessment of Simulations in Faust and Tascar for the Development of Audio Algorithms in Acoustic Environments - Code and Data
<p>Developing and testing audio algorithms with hard real-time constraints can be a complex task, requiring certain programming skills and/or<br>specialized equipment. However, many things can be tested in simulations on an ordinary computer, using <a href="https://tascar.org/" target="_blank" rel="noopener">TASCAR</a> for acoustic scene creation and<br><a href="https://faust.grame.fr/" target="_blank" rel="noopener">FAUST</a> for signal processing. Their capability are evaluated and compared to measurements using an FxLMS algorithm for active noise control as<br>example. This repository contains code and measured data of the publication “Assessment of simulations in FAUST and TASCAR for the development of<br>audio algorithms in acoustic environments”, presented at the International Faust Conference 2024 in Turin, Italy.</p>
Creating speech zones with self-distributing acoustic swarms (Simulated + Clutter)
<p>Datasets used in the paper: "Creating speech zones with self-distributing acoustic swarms"</p> <p>This deposit contains 2 distinct datasets: </p> <ol> <li>A dataset of speech mixtures containing 2-5 speakers simulated using PyRoomAcoustics. The dataset consists of 8000 training mixtures, 500 validation mixtures and 1000 testing mixtures.</li> <li>A dataset of speech mixtures containing 3-5 speakers created from synchronized recordings in reverberant rooms with objects cluttering the table. The dataset consists of 500 testing mixtures.</li> </ol> <p>The source sounds are various utterances from the VCTK dataset. For real world data, the utterances are played over a Rokono Bass+ Mini Speaker. The recordings are captured from an array of 7 microphones, as they are recorded by our robotic swarm as it is distributed across the table. The recorded audio in the real world has been subjected to audio compression and decompression using the Opus Codec to enable multiple simultaneous streams.</p> <p>Please see the Readme for more infromation. Please see related identifiers for other datasets.</p>
ASN Database - v3.2 - Database of Simulated Room Impulse Responses for Acoustic Sensor Networks Deployed in Complex Multi-Source Acoustic Environments
<p>We present a large set of simulated room impulse responses for a multi-room apartment. The simulated apartment models a real vacation apartment for which a recorded set of audio data has already been made available in the context of the DCASE challenges. The impulse responses were rendered using a dense grid of sources and receivers by means of a hybrid auralization algorithm based on a low-order image-source method and deterministic cone tracing. The proposed data set can be used to generate a wide variety of acoustic scenes which, in turn, can benefit numerous data-demanding machine-learning algorithms.<br> <br> To obtain more information on the database, please visit <a href="https://github.com/Jearde/asn-database">the website</a>.<br> <strong>Please read the license file (available in the GitHub repository) before using the database.</strong></p>
Data for: Collective signalling is shaped by feedbacks between signaller variation, receiver perception, and acoustic environment in a simulated communication network
Open the record for dataset details and reuse information.
A Wholly Analytical Method for the Simulation of an Electromagnetic Acoustic Transducer Array
<p>These data sets are for the paper "A Wholly Analytical Method for the Simulation of an Electromagnetic Acoustic Transducer Array" which will be published in the International Journal of Applied Electromagnetics and Mechanics.</p> <p>All of the data are responding to the figures shown in this article.</p>
NEECK Validation: Acoustic Measurements and BEM Simulations
<p>This repository contains the supporting data for the paper entitled "Acoustic Validation of a BEM-Suitable 3D Mesh Model of KEMAR'', K. Young, G. Kearney, and A. I. Tew, at the 2018 AES International Conference on Spatial Reproduction - Aesthetics and Science, Tokyo. Available at: http://www.aes.org/e-lib/browse.cfm?elib=19662. Please cite both the paper and dataset if used.</p> <p>Note: the azimuth angle system used in this work increments positively in the left direction, such that 90° is on the left and 270° is on the right. In elevation, -90° is below, +90° is above. </p> <p>---</p> <p>The data is organised as follows:</p> <p>- NEECK_HRIR_measured.sofa<br> (SOFA file (SimpleFreeFieldHRIR) containing the 185 acoustically measured HRIRs for the Neck-Extended Easily Computable KEMAR (NEECK))<br> - NEECK_HRTF_simulated.sofa<br> (SOFA file (SimpleFreeFieldTF) containing the 10,205 simulated HRTFs for the Neck-Extended Easily Computable KEMAR (NEECK))<br> - AdditionalData<br> (Zip folder containing data processed during the analysis stages)<br> - averageResponse_measured.mat<br> (mat file containing the average IR responses, corresponding inverse filters and inverse filter generation parameters)<br> - averageResponse_simulated.mat<br> (mat file containing the average TF responses in linear scale)<br> - measuredData.mat<br> (mat file containing the following data:)<br> - IRs<br> (Measured impulse responses as in SOFA file. Dimensions: M1xRxN1)<br> - IRs_DTF<br> (Impulse responses after application of average response inverse filter. Dimensions: M1xRxN1)<br> - HRTFs<br> (HRTF magnitudes in linear scale. Dimensions: M1xRxN1)<br> - HRTFs_dB<br> (As above in decibel scale. Dimensions: M1xRxN1)<br> - DTFs<br> (DTF magnitudes in linear scale - after application of average response inverse filter. Dimensions: M1xRxN1)<br> - DTFs_dB<br> (As above in decibel scale. Dimensions: M1xRxN1)<br> - measFs<br> (sampling rate of measured responses: Dimensions: 1x1)<br> - allSourcePositions_measured<br> (measured source positions in spherical coordinates (azimuth, elevation, radius). Units: degrees, degrees, metres. Dimensions: M1x3)<br> - simulatedData.mat<br> (mat file containing the following data:<br> - IRs<br> (Impluse responses generated from the simulated HRTF data. Dimensions: M2xRxN2)<br> - HRTFs_complex<br> (Complex simulated HRTF data. Dimensions: M2xRxN3)<br> - HRTFs_mag_dB<br> (Magnitudes of simulated HRTF data in decibel scale. Dimensions: M2xRxN3)<br> - DTFs_mag_lin<br> (Magnitudes of directional transfer function (DTF) data in linear scale. Dimensions: M2xRxN3)<br> - DTFs_mag_dB<br> (As above in decibel scale. Dimensions: M2xRxN3)<br> - simFs<br> (sampling rate of generated impulse responses. Dimensions: 1x1)<br> - allSourcePositions_simulated<br> (simulated source positions in spherical coordinates (azimuth, elevation, radius). units: degrees, degrees, metres. Dimensions: M2x3)<br> - frequencies<br> (frequencies used in the simulation. Dimensions: N3x1)<br> - license.mat<br> (mat file containing licensing information)<br> - License.txt<br> (Text file detailing the license under which this data is published.)</p> <p>For enquiries regarding the data in a different format, please email kaey500@york.ac.uk. <br> ---</p> <p>Data Dimensions:</p> <p>M1 = number of measured source positions, in this case 185<br> M2 = number of simulated source positions, in this case 10,205<br> R = number of channels, in this case 2, where 1 and 2 correspond to left and right respectively<br> N1 = number of samples in measured impulse responses, in this case 1024<br> N2 = number of samples in generated impulse responses, in this case (number of samples in HRTF*2)+2 = 400<br> N3 = number of samples in simulated transfer functions, in this case, the number of frequency points, 199</p> <p>---</p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), with no warranty; or the implied warranty of merchantability or fitness for a particular problem.</p> <p>---</p> <p>Data produced by Kat Young at the AudioLab, Dept. of Electronic Engineering, University of York.<br> Contact: kaey500@york.ac.uk</p>
Dataset and visualization of numerical simulations of decaying acoustic turbulence in three dimensions
<p>This dataset contains the time series and spectrum data for the 1000^3 resolution simulations featured in the paper: <em>Primordial acoustic turbulence: three-dimensional simulations and gravitational wave predictions</em> by Jani Dahl, Mark Hindmarsh, Kari Rummukainen, and David Weir. Also included are the various output files produced by the simulation code, the run files that can be used to reproduce the runs, and plots of the time series quantities, fluid snapshots, and movies of the longitudinal and transverse energy spectra.</p>
Beam-driven Electron Cyclotron Harmonic and Electron Acoustic Waves as Seen in Particle-In-Cell Simulations
<p>Data repository for ``Beam-driven Electron Cyclotron Harmonic and Electron Acoustic Waves as Seen in Particle-In-Cell Simulations''. This repository contains particle-in-cell simulations of beam-driven electron cyclotron harmonic and whistler-mode waves.</p>
Supplementary material - Blind comparison of binaural auralisations to a real loudspeaker in an audiovisual virtual classroom scenario: Effect of room acoustic simulation, HRTF dataset and head worn devices on rated room-acoustical attributes
<p>Additional Material for the paper "Blind comparison of binaural auralisations to a real loudspeaker in an audiovisual virtual classroom scenario: Effect of room acoustic simulation, HRTF dataset and head worn devices on rated room-acoustical attributes".</p> <p> </p> <p>This work is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) under the project ID 422686707, SPP2236 – AUDICTIVE – Auditory Cognition in Interactive Virtual Environments</p>
Supplementary Materials to the paper "Simulation of infrasonic acoustic wave imprints on mesopause airglow layers during the 2016 M7.8 Kaikoura earthquake" by Inchin et al., (2021)
<p>The archive contains datasets, animations, scripts and other supplementary materials to the paper "Simulation of infrasonic acoustic wave imprints on mesopause airglow layers during the 2016 M7.8 Kaikoura earthquake".</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)
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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.