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Dataset results
9 results for “3D Audio”
Pyramic Dataset : 48-Channel Anechoic Audio Recordings of 3D Sources
<p>The Pyramic Dataset contains recordings done using the<br> <a href="https://github.com/LCAV/Pyramic">Pyramic</a> 48 channel microphone array in an<br> anechoic chamber. The recordings consist of 8 different samples (2x sweeps, 1x<br> noise, 5x speech) repeated at 180 angles (every 2 degrees) and from 3 different<br> heights. The audio samples recorded are</p> <ul> <li>Linear and exponential sweeps</li> <li>Noise sequence</li> <li>2x male and 3x female speech</li> </ul> <p>This dataset allows to evaluate the performance of array processing algorithms<br> on real-life recordings done using MEMS microphones similar to those used in<br> mobile phones with all the non-idealities involved. The dataset is suitable for both 2D<br> and 3D scenarios. By subsampling the 48<br> microphones, a large number of array configurations can be tested. Example of<br> algorithms are:</p> <ul> <li>Direction of arrival (DOA) estimation</li> <li>Beamforming</li> <li>Source separation</li> <li>Array calibration</li> </ul> <p>Another application is the generation of realistic room impulse by combining<br> the impulse responses of microphones from sources at multiple angles with a<br> variant of the image source model.</p> <p>In addition to the raw (compressed or not) and segmented<br> recordings, the impulse responses of all the microphones for every source<br> locations were recovered from the exponential sweep measurements and are<br> distributed together with the dataset. The initial manual measurement of loudspeakers<br> and microphones locations was improved upon using a blind calibration method.</p> <p>This record contains</p> <ul> <li>The compressed recordings (TTA format)</li> <li>Segmented recorded samples</li> <li>Impulse responses</li> <li>Documentation and code (also available on <a href="https://github.com/fakufaku/pyramic-dataset">github</a>)</li> </ul> <p>The raw measurements in wav format are available as a separate <a href="https://zenodo.org/deposit/1209005">record</a> (10.5281/zenodo.1209005).</p> <p>The best way to get started is to only get the documentation and code from <a href="https://github.com/fakufaku/pyramic-dataset">github</a> (a copy is available in pyramic-dataset-doc-d2a456b4.zip) and follow the instructions in the README. The version on github is most up-to-date. If possible, please use that one.</p>
Pyramic Dataset : 48-Channel Anechoic Audio Recordings of 3D Sources (Raw)
<p>The Pyramic Dataset contains recordings done using the<br> <a href="https://github.com/LCAV/Pyramic">Pyramic</a> 48 channel microphone array in an<br> anechoic chamber. The recordings consist of 8 different samples (2x sweeps, 1x<br> noise, 5x speech) repeated at 180 angles (every 2 degrees) and from 3 different<br> heights. The audio samples recorded are</p> <ul> <li>Linear and exponential sweeps</li> <li>Noise sequence</li> <li>2x male and 3x female speech</li> </ul> <p>This dataset allows to evaluate the performance of array processing algorithms<br> on real-life recordings done using MEMS microphones similar to those used in<br> mobile phones with all the non-idealities involved. The dataset is suitable for both 2D<br> and 3D scenarios. By subsampling the 48<br> microphones, a large number of array configurations can be tested. Example of<br> algorithms are:</p> <ul> <li>Direction of arrival (DOA) estimation</li> <li>Beamforming</li> <li>Source separation</li> <li>Array calibration</li> </ul> <p>Another application is the generation of realistic room impulse by combining<br> the impulse responses of microphones from sources at multiple angles with a<br> variant of the image source model.</p> <p>In addition to the raw (compressed or not) and segmented<br> recordings, the impulse responses of all the microphones for every source<br> locations were recovered from the exponential sweep measurements and are<br> distributed together with the dataset. The initial manual measurement of loudspeakers<br> and microphones locations was improved upon using a blind calibration method.</p> <ul> </ul> <p>This record contains only the raw measurements in wav format and archive of the documentation and code.</p> <p>The post-processed data is available in a separate <a href="https://zenodo.org/record/1209563">record</a> that contains:</p> <ul> <li>The compressed recordings (TTA format)</li> <li>Segmented recorded samples</li> <li>Impulse responses</li> <li>Documentation and code (also available on <a href="https://github.com/fakufaku/pyramic-dataset">github</a>)</li> </ul> <p>The best way to get started is to only get the documentation and code from <a href="https://github.com/fakufaku/pyramic-dataset">github</a> and download the data as needed into the unzipped archive. Then follow the instructions in README.md.</p>
3D printer audio and vibration side channels
<p>The dataset focuses on side channel data, i.e., sound and vibration, of 3D printers. The dataset aims to enable further research in cyber-physical system security and explore the vulnerability of fused deposition modeling to side chanel attacks.</p> <p>In particular, the datset consists mainly of sound and vibration data that were collected from two different 3D printers (bambu lab P1P and A1mini), using two different sensor systems. The first method is based on an iPhone, whereas the second one is based on a Teensy microcontroller. Both systems record sound at 44.1kHz sampling frequency. The vibrations are based on acceleration data sampled at 100 Hz and 500 Hz for the iPhone and the Teensy 4.0, respectively. Furthermore, the diversity of the dataset for both Teensy and iPhone methods was achieved using 12 different 3D designs. The dataset also inludes the source 3D CAD and sliced toolpath files of the objects that were printed, videos of the printing process, and recordings of the backround noise that exists in the data recordings. A table of contents is also provided in the files.</p>
Audio and 3D flight-track recordings of mosquito responses to opposite-sex sound-stimuli
Open the record for dataset details and reuse information.
Audio from: 'Modeling Voiced Stop Consonants using the 3D Dynamic Digital Waveguide Mesh Vocal Tract Model'
<p>Audio files associated with the paper 'Modeling Voiced Stop Consonants using the 3D Dynamic Digital Waveguide Mesh Vocal Tract Model', presented at the International Congress of Phonetic Sciences 2019, Melbourne, Australia.</p>
AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments - Supplementary Data
<p><strong>Introduction</strong></p> <p>In this zip, we release the auxiliary data that is beneficial to execute the implementation of AVLEN described in our paper AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments by Sudipta Paul, Amit K Roy-Chowdhury, and Anoop Cherian, NeurIPS, 2022.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped data is 4.6G</li> <li>The unzipped folder contains: (i) a README.md file and (ii) ./AVLEN-data folder. The latter contains the following zip files. Please see the AVLEN code to see how to unzip these files into their respective folders. <ul> <li>ckpt.119.pth -- 61M </li> <li>connectivity.zip -- 1.4M </li> <li>pretrained_weights.zip -- 1.7G</li> <li>ResNet-152-imagenet.zip -- 2.9G</li> <li>semantic_audionav_dialog_approx.zip -- 2.7M</li> <li>soundspaces.zip -- 479K</li> <li>speaker_model_weights.zip -- 51M</li> </ul> </li> </ul> <p><strong>Other Resources</strong></p> <p>For the implementation of AVLEN that uses the data shared here, please visit <a href="https://www.merl.com/publications/TR2022-131">MERL TR2022-131</a>.</p> <p><strong>Citation</strong></p> <p>If you use AVLEN in your research, please cite our paper:</p> <pre><code>@InProceedings{paul2022avlen, title={AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments}, booktitle={Advances in Neural Information Processing Systems}, author={Paul, Sudipta and Roy-Chowdhury, Amit and Cherian, Anoop}, volume={35}, pages={6236--6249}, year={2022} }</code></pre> <p><strong>Copyright and License</strong></p> <p>The AVLEN dataset is released under CC-BY-SA-4.0 license.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre> <p> </p>
Field Report on 3D Audio Capture of Solo Piano for Classical Music Productions - Audio Files
<p>This online repository contains audio files related to research on the side surround loudspeakers on 3D audio recordings of solo piano in classical music productions, undertaken by Emre Ekici, Will Howie, and Toru Kamekawa at Tokyo University of the Arts, March 2023. Please find the guidelines for usage below: </p> <p>The archive (3DPIANO_TRACKS.zip) contains 23 channels of mono audio tracks for ITU 4+7+0 solo piano recording, played by Yamaha Disklavier. Files are recorded at 96 kHz / 24-bit.</p> <p>File naming convention:<br> ##_ProjectTitle_Loudspeaker_Position/Variable_Position/Variable_Option (if applicable).</p> <p>Anyone is free to download and listen to these files for reference.</p> <p>If you wish to use these files for your research, please contact Emre Ekici (mrekici@outlook.com) or Will Howie (wghowie@gmail.com) for permission.</p>
Comparison of 2D and 3D Multichannel Audio Rendering Methods for Hearing Research Applications using Technical and Perceptual Measures - Impulse Responses and Scene Recordings
<p>This database contains recordings of impulse responses (IRs.zip) and virtual acoustic scenes (scenes.zip), with different 2D and 3D multichannel loudspeaker rendering methods. This upload contains conplementary data to [1].</p> <p>The virtual acoustic scenes are a 'concert' of an orchestra with approximately 60 primary sound sources [2] in a reverberant room, a 'speech' scene with a single talker in the same room, and a 'street' scene with static and moving sound sources.</p> <p>Recordings were performed with a G.R.A.S. 45BB Head and Torso Simulator (Kemar) placed in the center of the loudspeaker array in the lab, with ears at 1.60 m height. Recordings include the left and right ear channel. The simulator was equipped with large anthropometric pinnae of type KB5001. All recordings are provided for each rendering method that was applied in the study [1].</p> <p>Impulse responses were recorded using sine sweeps [3], sampling frequency was 44100 Hz. The impulse responses were truncated to 1.2 s. The scenes were recorded with a sampling frequency 44100 Hz.</p> <p> </p> <p>Files are named with the following convention:</p> <p>filetype_scene_renderingmethod_source.[mat/wav]</p> <p> </p> <p>References:</p> <p>[1] M. Gerken, V. Hohmann, G. Grimm, "Comparison of 2D and 3D Multichannel Audio Rendering Methods for Hearing Research Applications using Technical and Perceptual Measures," Acta Austica 2024, in press.</p> <p>[2] C. Böhm, D. Ackermann, and S. Weinzierl, "A Multi-channel Anechoic Orchestra Recording of Beethoven's Symphony No. 8 op. 93," Journal of the Audio Engineering Society, vol. 68, no. 12, pp. 977–984, Jan. 2021, doi: 10.17743/jaes.2020.0056.</p> <p>[3] A. Farina, "Simultaneous Measurement of Impulse Response and Distortion with a Swept-Sine Technique," in Audio Engineering Society Convention 108, Feb. 2000. [Online]. Available: http://www.aes.org/e-lib/browse.cfm?elib=10211</p>
M7: Comparing 8-inch sized Loudspeakers for a 3D Audio Production Lab
<p>This data set includes a report on the loudspeaker selection process for the Ligeti Zentrum's 3D Audio Production Lab. It also contains the developed comparison framework, as well as simulation and measurement data.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.