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23 results for “Auralization”
Auralization of aircraft
<p>This dataset consists of 4 files, and are a preliminary result. </p> <ul> <li>recording.wav is a recording of an Airbus A320 taken nearby Zurich airport,</li> <li>reverted.wav is the signal that is obtained after backpropagating in time-domain,</li> <li>synthesis.wav is a signal created from features (tones/noise) obtained through an automatic analysis of the backpropagated signal,</li> <li>auralization.wav is what one would hear at the receiver after propagating again from source to receiver. Ideally, this signal matches with the recording.</li> </ul> <p> </p>
Auralization of virtual microphone array sensors considering coherence loss by atmospheric turbulence for two moving monopole sources
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Initial auralization of a distributed propulsion system equipped with 26 ducted low-speed fans
<p>Illustration of engine noise auralization by DLR Institute of Propulsion Technology obtained with the framework PropNoise, VIOLIN, CORAL. Data associated with the following publication: S. Schade, R. Merino-Martinez, P. Ratei, S. Bartels, R. Jaron and A. Moreau, "<a href="https://doi.org/10.2514/6.2024-3273"><em>Initial Study on the Impact of Speed Fluctuations on the Psychoacoustic Characteristics of a Distributed Propulsion System with Ducted Fans</em></a>", 30th AIAA/CEAS Aeroacoustics Conference, Rome, Italy, 04-07 June, 2024.</p> <p>Selected binaural audio files to illustrate the impact of rotational speed fluctuations on the noise characteristics of a distributed propulsion system equipped with 26 ducted, low-speed fans. Please note that the sound pressure amplitudes are normalized to 110dB for the reference turbofan case and to 90dB for the cases with distributed fans.</p> <p>The corresponding time signals and spectrograms are available in the associated conference paper in Figures 4-6.</p>
zEPHYR - Audio files of recorded and auralized wind turbine noise
<p>Audio files associated with the publication "Wind farm noise prediction and auralization", Andrea P. C. Bresciani, Julien Maillard, Arthur Finez, submitted to Acta Acustica in Dec. 2023.</p> <p>Audio 1: Auralized noise for OC1 and SB1<br>Audio 2: Auralized noise for OC1 and SB2<br>Audio 3: Auralized noise for OC1 and SB3<br>Audio 4: Auralized noise for OC2 and SB1<br>Audio 5: Auralized noise for OC2 and SB2<br>Audio 6: Auralized noise for OC2 and SB3<br>Audio 7: Recorded noise for OC1 and SB1<br>Audio 8: Recorded noise for OC1 and SB2<br>Audio 9: Recorded noise for OC1 and SB3<br>Audio 10: Recorded noise for OC2 and SB1<br>Audio 11: Recorded noise for OC2 and SB2<br>Audio 12: Recorded noise for OC2 and SB3<br>Audio 13: Auralized noise for OC2 and SB2 without amplitude fluctuations</p>
Photogrammetry-based model of the Copacabana for auralization and audio-visual perception studies in virtual reality
<p>This dataset contains photogrammetry-based audio-visual models from Copacabana. They are used for urban sound auralization as well as for audio-visual perception studies in virtual reality.</p> <p>Photogrammetry model available in the following formats: 3ds, dae, dxf, fbx, obj, stl</p> <p>Simplified CAD model for acoustic simulations available in format: dae</p>
Anechoic and IR Convolution-based Auralization Data Compilation Ensemble (AIRCADE)
<p><strong>AIRCADE</strong> is a data-compilation ensemble, primarily intended to serve as a resource for researchers in the field of dereverberation, particularly for data-driven approaches. It comprises <strong><a href="https://zenodo.org/record/1188976#.ZDhNTHbMJPY">speech and song samples</a></strong>, together with <strong><a href="https://zenodo.org/record/3371780#.ZDhOC3bMJPZ">acoustic guitar sounds</a></strong>, with original annotations pertinent to emotion recognition and Music Information Retrieval (MIR). Moreover, it includes a selection of <strong><a href="https://www.openair.hosted.york.ac.uk/">Impulse Response (IR) samples</a></strong> with varying Reverberation Time (RT) values, providing a wide range of conditions for evaluation. This data-compilation can be used together with provided Python scripts (available on <strong><a href="http://github.com/TulioChiodi/AIRCADE">GitHub</a></strong>), for generating auralized data ensembles in different sizes: <em>tiny</em>, <em>small</em>, <em>medium</em> and <em>large</em>. Additionally, the provided metadata annotations also allow for further analysis and investigation of the performance of dereverberation algorithms under different conditions. All data is licensed under <strong><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">Creative Commons Attribution 4.0 International License</a></strong>.</p> <p><strong>About the sizeable versions:</strong></p> <p>The data-compilation is hosted here at <strong><a href="https://zenodo.org/record/7818761#.ZD7ON3bMJPa">Zenodo</a></strong>, with an approximate total file size of 1.3 GB. For simplicity, all samples in our data-compilation were renamed, e.g., <em>guitar_0000</em>, <em>rir_0000</em>, <em>song_0000</em>, <em>speech_0000</em>, and so on. The ensemble versions are available in different sizes, from a <em>tiny</em> version, with limited data, to a <em>large</em> version, with almost 300,000 samples. This allows users to choose the most suitable version for their specific research needs. The following table illustrates the differences between all versions, detailing the number of song, speech, guitar, IR and auralized samples in each one, together with their respective total file size and duration.</p> <table align="center"> <caption>Number of anechoic, IR and resultant auralized data samples, together with their respective total duration and file size for each ensemble version</caption> <tbody> <tr> <td><strong>Version</strong></td> <td><strong>Tiny</strong></td> <td><strong>Small</strong></td> <td><strong>Medium</strong></td> <td><strong>Large</strong></td> </tr> <tr> <td>Song samples</td> <td>100</td> <td>500</td> <td>1,012</td> <td>1,012</td> </tr> <tr> <td>Speech samples</td> <td>100</td> <td>500</td> <td>1,012</td> <td>1,440</td> </tr> <tr> <td>Guitar samples</td> <td>100</td> <td>500</td> <td>1,012</td> <td>2,004</td> </tr> <tr> <td>IR samples</td> <td>5</td> <td>9</td> <td>33</td> <td>65</td> </tr> <tr> <td>Auralized samples</td> <td>1,500</td> <td>13,500</td> <td>100,188</td> <td>289,640</td> </tr> <tr> <td>Total duration</td> <td>3.2 h</td> <td>30.41 h</td> <td>221.77 h</td> <td>658.08 h</td> </tr> <tr> <td>Total file size (required)</td> <td>1.1 GB</td> <td>10.5 GB</td> <td>76.6 GB</td> <td>227.5 GB</td> </tr> </tbody> </table> <p>For more information, please refer to our data paper on <strong><a href="https://arxiv.org/abs/2304.09318">ArXiv</a></strong>.</p> <p><strong>Citation</strong>:</p> <p>If you find <strong>AIRCADE </strong>useful in your research, please cite:</p> <blockquote> <pre>@misc{chiodi2023aircade, title={AIRCADE: an Anechoic and IR Convolution-based Auralization Data-compilation Ensemble}, author={Túlio Chiodi and Arthur dos Santos and Pedro Martins and Bruno Masiero}, year={2023}, eprint={2304.09318}, archivePrefix={arXiv}, primaryClass={eess.AS} }</pre> </blockquote> <p><strong>Acknowledgement</strong>:</p> <p>This work was partially supported by the <strong><a href="https://fapesp.br/">São Paulo Research Foundation (FAPESP)</a></strong>, grants #2017/08120-6 and #2019/22795-1.</p>
Comparison Auralization vs. Measurements of V2500 engine flyover at take-off conditions
<p>Illustration of engine noise auralization by DLR Institute of Propulsion Technology obtained with the framework PropNoise, VIOLIN, CORAL. Data associated with publication: “A framework to simulate and to auralize the sound emitted by aircraft engines.” paper Nr. C001073, InterNoise Conference, Chiba, 2023 by A. Moreau, A. Prescher, S. Schade, M. Dang, R. Jaron, S. Guérin.</p> <p>Examples of audio files for the simulation and auralization of two engine flyover experiments at take-off conditions (V2527 engine powering A320 civil aircraft):</p> <ul> <li>Flyover A – experiment on DLR LNATRA research aircraft, measurements are monaural and taken with a microphone placed wall flush with the ground (no ground reflections)</li> <li>Flyover B – experiment at Berlin Airport, measurements are taken at 1.2m above ground with an artificial head equipped with two microphones, binaural recording and ground reflection.</li> </ul> <p>Sound amplitude levels have been normalized.</p>
Greek Theatre of Tyndari: auralized binaural tracks
<ul> <li><em>IPHIGENIA-Singer_rotation_Receiver_1-Source_1.wav</em> (The singer starts singing by pointing at receiver R1, makes two turns around herself in a clockwise direction and finishes by pointing at receiver R1 again)</li> <li><em>IPHIGENIA-Singer_rotation_Receiver_6-Source_1.wav</em> (The singer starts singing by pointing at receiver R6, makes two turns around herself in a clockwise direction and finishes by pointing at receiver R6 again)</li> </ul>
Auralizations of Current and Future Aircraft Concepts
<p>The noise of the four flyovers in this video are purely synthetic sound - so called auralizations.</p> <p>In the Horizon 2020 research project ARTEM (Aircraft noise Reduction Technologies and related Environmental iMpact: <a href="https://cordis.europa.eu/project/id/769350">https://cordis.europa.eu/project/id/769350</a>) funded by the European Union, these four presented and 40 more auralizations were used in a psychoacoustic laboratory experiment, conducted at Empa Dübendorf, to investigate the noise annoyance to aircraft flyovers of a future aircraft design compared to a current commercial aircraft.</p> <p> </p> <p>[1] R. Pieren, I. LeGriffon, L. Bertsch, A. Heusser, F. Centracchio, D. Weinstraub, C. Lavandier, and B. Schäffer, "Perception-based noise assessment of a future blended wing body aircraft concept using synthesized flyovers in an acoustic VR environment – the ARTEM study", Aerosp. Sci. Technol., vol. 144, 2024, doi.org/10.1016/j.ast.2023.108767</p> <p>[2] B. Schäffer, L. Bertsch, I. Le Griffon, A. Heusser, C. Lavandier, and R. Pieren, "Evaluation of flyover auralizations of today's and future long-range aircraft concepts", International Congress and Exposition on Noise Control Engineering (InterNoise), Glasgow, 21-24 August 2022.</p> <p>[3] R. Pieren and D. Lincke, "Auralization of aircraft flyovers with turbulence-induced coherence loss in ground effect", J. Acoust. Soc. Am., vol. 151, no. 4, pp. 2453-2460, 2022.</p> <p>[4] R. Pieren, L. Bertsch, D. Lauper, and B. Schäffer, "Improving future low-noise aircraft technologies using experimental perception-based evaluation of synthetic flyovers", Sci. Total Environ., vol. 692, pp. 68-81, 2019.</p>
H2020 ENODISE: ONERA Auralizations Configuration B2
<p>A quick evaluation of the SISW numerical data (<a href="https://zenodo.org/record/8176756">https://zenodo.org/record/8176756) </a>and the ECL experimental data (<a href="https://zenodo.org/record/7925336">https://zenodo.org/record/7925336</a>) was done on an auditory level through auralisations. The in-house auralisation tool FLAURA was used. Sound signatures were evaluated at the microphone positions 17 and 22 (at 90° to the propeller plane) for single and double propeller configurations, with and without flow.</p> <p>The database is composed of .wav files, presenting the measured temporal data (ECL) either compared to auralised measured spectral data (ECL) or simulated data (SISW).</p> <p>A description of the data is included in a pdf file.</p>
Auralization of amplitude fluctuations in aircraft flyover
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Rendered Stimuli for "Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room"
<h2>Rendered Stimuli from the Subjective Evaluation</h2> <p>This archive (<code>Stimuli.zip</code>) contains the rendered stimuli used in the subjective evaluation of various spatial analysis and synthesis methods, as described in the paper "Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room" by Alan Pawlak, Hyunkook Lee, Aki Mäkivirta, and Thomas Lund.</p> <p>The stimuli are provided to improve the reproducibility of the study and to allow readers to listen to the same audio samples used in the subjective evaluation.</p> <h2>File Naming Convention:</h2> <p><code>SYSTEM_PROGRAMMEMATERIAL_AZIMUTH_ELEVATION_-26LUFS.wav</code></p> <p>- <code>SYSTEM</code>: The spatial analysis and synthesis method used (e.g., BSDM-6OM1-Omni, HO-SIRR, SDM-em32, etc.)<br>- <code>PROGRAMMEMATERIAL</code>: The anechoic audio sample used (Bongo, Speech, Orchestra)<br>- <code>AZIMUTH</code>: The azimuth angle of the sound source (e.g., 0, 30, 45, 90, 135)<br>- <code>ELEVATION</code>: The elevation angle of the sound source (e.g., 0, 45)</p> <h2>Audio File Specifications:</h2> <p>- Format: WAV<br>- Sample Rate: 48 kHz<br>- Bit Depth: 32-bit<br>- Loudness Normalization: -26 LUFS</p> <p>To use these stimuli, simply load the desired WAV file into your audio playback software.</p> <p>For more information about the study, please refer to the full paper.</p> <p>Pawlak, A., Lee, H., Mäkivirta, A. and Lund, T., 2024. Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room.</p>
Model for random atmospheric inhomogeneities in engine noise auralization: Audio files for validation
<p>Illustration of engine noise auralization by DLR Institute of Propulsion Technology obtained with the framework PropNoise, VIOLIN, CORAL. Data associated with the following publication: A. Prescher, A. Moreau, S. Schade, "<a href="https://doi.org/10.1007/s13272-024-00764-4" target="_blank" rel="noopener"><em>Model for random atmospheric inhomogeneities in engine noise auralization</em></a>", CEAS Aeronautical Journal, 2024.</p> <p>Selected binaural audio files to illustrate the impact of random atmospheric inhomogenities on the noise characteristics of a turbofan engine.</p> <p>The corresponding time signals and spectrograms are available in the associated paper in Figure 7.</p>
Treatment Outcomes for Temporomandibular Disorders (TMD) Via the Clayton Intra-aural Device (CID) Clinical Trial
ClinicalTrials.gov study NCT00815776. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Dataset: "Auralization of Electric Vehicles for the Perceptual Evaluation of Acoustic Vehicle Alerting Systems"
<p>This repository contains audio examples and measurement data accompanying the paper: </p> <blockquote> <p>Müller L. & Kropp W. 2024. Auralization of electric vehicles for the perceptual evaluation of acoustic vehicle alerting systems. Acta Acustica, 8, 27. https://doi.org/10.1051/aacus/2024025</p> </blockquote> <p>The Matlab code for the corresponding auralization model can be found at: <a href="https://github.com/leonpaulmueller/evat" target="_blank" rel="noopener">https://github.com/leonpaulmueller/evat</a></p> <p> </p> <p><strong>Content</strong></p> <ul> <li><code>audio_examples.zip</code> <ul> <li>avas - Measured and synthesized AVAS source signals</li> <li>passby - Measured and auralized binaural EV passages at roadside observer position. The generated signals use the same vehicle velocity as the corresponding measurements.</li> <li>tire - Measured and synthesized tire/road noise source signals</li> </ul> </li> <li><code>measurements.zip</code> <ul> <li>ambience - binaural ambience measurements</li> <li>avas - AVAS source signal measurements</li> <li>passby - Binaural pass-by measurements, including velocity data and isolated AVAS and tire/road noise signals</li> <li>tires - tire/road noise measurements</li> </ul> </li> </ul> <p> </p> <p>For consistency with the paper, we use the following aliases for the three evaluated vehicles:</p> <ul> <li>Vehicle A: Tesla Model Y 2021</li> <li>Vehicle B: Volkswagen ID.3 Pro Performance 2021</li> <li>Vehicle C: Nissan Leaf 2018</li> </ul>
Binaural auralizations of listening experiment stimuli (Envelopment / Engulfment / Spatial Granular Synthesis)
<p>Binaural auralizations of experiment stimuli with KU100 BRIRs, measured at the listening position of the experiments in the IEM CUBE (25-channel loudspeaker hemisphere). The files are 2-channel WAVs for headphone playback.</p>
Aural Rehabilitation for Cochlear Implant Users Via Telerehab Technology
ClinicalTrials.gov study NCT03157492. IPD Sharing: NO. Countries: 1. Publications: 5.
The Hearing Aid Effectiveness After Aural Rehabilitation (HEAR) Trial
ClinicalTrials.gov study NCT00260663. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Effect of an Aural Rehabilitation Program in Hearing-impaired Older Adults
ClinicalTrials.gov study NCT05083221. IPD Sharing: NO. Countries: 1. Publications: 1.
Binaural auralizations of listening experiment stimuli (acoustic source models / LEV)
<p>Binaural auralizations (two-channel wav files) of loudspeaker experiment stimuli. </p>
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