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17 results for “anechoic”
Anechoic McVAMPIRE – Anechoic Multichannel Varying Mouth Position Impulse Response Dataset
<p>This dataset contains impulse responses (IRs) that were recorded in an anechoic room. The recording setup imitates the geometry of a minivan with eight seats arranged in three seat rows. The IRs were captured with 14 overhead microphones positioned in the imaginary car roof using a built-in mouth simulator of a head and torso simulator (HATS) at eight passenger seat positions with eleven orientations each. In addition, the dataset contains IRs measured with four lateral loudspeakers imitating door loudspeakers, as well as a noise floor recording.</p> <p>This dataset supplements the <a href="https://doi.org/10.5281/zenodo.12806684">In-Car McVAMPIRE</a> dataset which was captured with an identical microphone setup in a real car. Both datasets can be used to simulate speech in a car from different seats with different speaker orientations including the loudspeaker-enclosure-microphone (LEM) system under anechoic or realistic, reverberant conditions.</p>
AID: Open-Source Anechoic Interferer Dataset
<p>A dataset of anechoic recordings of various sound sources encountered in domestic environments is provided, which is intended to be a resource of non-stationary, environmental noise signals that, when convolved with acoustic impulse responses, can be used to simulate complex acoustic scenes.</p> <p>The dataset consists of anechoic recordings of 43 different types of sound sources encountered in domestic environments, with the number of individual recordings per sound source varying between two and eleven. The sound sources, which are mostly household devices and utilities, include door keys, plastic bags, clothing, a drilling machine, an electric blender, glass jars and metal boxes but also a few human-made sounds, such as clapping, breathing, snapping or whistling. The recordings cover a wide range of timbres. Multiple sounds from every individual source were recorded by different ways of excitation, such as hitting and shaking, or switching on and off the electric devices. Three different microphones were used to record the various sound sources.</p> <p>In addition, a <em>Python</em> library is provided that can be used to randomly arrange multiple anechoic noise recordings into a single channel interference signal. The number of individual recordings concurrently playing at any point in time in an interference signal can be specified by the user, providing control over the temporal density. The signal generator implementation is hosted on <a href="https://github.com/audiolabs/anechoic-noise">GitHub</a>.</p>
PHENICX-Anechoic: note annotations for Aalto anechoic orchestral database
<p><strong>PHENICX-Anechoic: denoised recordings and note annotations for Aalto anechoic orchestral database</strong></p> <p> </p> <p> </p> <p> </p> <p><strong>Description </strong></p> <p> </p> <p>This dataset includes audio and annotations useful for tasks as score-informed source separation, score following, multi-pitch estimation, transcription or instrument detection, in the context of symphonic music.</p> <p> </p> <p>This dataset was presented and used in the evaluation of:</p> <p> </p> <p>M. Miron, J. Carabias-Orti, J. J. Bosch, E. Gómez and J. Janer, "Score-informed source separation for multi-channel orchestral recordings", Journal of Electrical and Computer Engineering (2016))"</p> <p> </p> <p>On this web page we do not provide the original audio files, which can be found at the web page hosted by Aalto University. However, with their permission we distribute the denoised versions for some of the anechoic orchestral recordings:</p> <p> </p> <p>Pätynen, J., Pulkki, V., and Lokki, T., "Anechoic recording system for symphony orchestra," <em>Acta Acustica united with Acustica</em>, vol. 94, nr. 6, pp. 856-865, November/December 2008.</p> <p> </p> <p>For the intellectual rights and the distribution policy of the audio recordings in this dataset contact Aalto University, Jukka Pätynen and Tapio Lokki. For more information about the original anechoic recordings we refer to the web page and the associated publication [2]</p> <p> </p> <p>We provide the associated musical note onset and offset annotations, and the Roomsim[3] configuration files used to generate the multi-microphone recordings [1].</p> <p> </p> <p>The anechoic dataset in [2] consists of four passages of symphonic music from the Classical and Romantic periods. This work presented a set of anechoic recordings for each of the instruments, which were then synchronized between them so that they could later be combined to a mix of the orchestra. In order to keep the evaluation setup consistent between the four pieces, we selected the following instruments: violin, viola, cello, double bass, oboe, flute, clarinet, horn, trumpet and bassoon.</p> <p> </p> <p>We created a ground truth score, by hand annotating the notes played by the instruments. The annotation process involved gathering the original scores in MIDI format, performing an initial automatic audio-to-score alignment, then manually aligning each instrument track separately with the guidance of a monophonic pitch estimation.</p> <p> </p> <p>During the recording process detailed in [2], the gain of the microphone amplifiers was fixed to the same value for the whole process, which reduced the dynamic range of the recordings of the quieter instruments. This lead to problems with which we had to deal, in order to reduce the noise. In the paper we described the score-informed denoising procedure we applied to each track.</p> <p> </p> <p>A complete description of the dataset and the creation methodology, including the generation of the multi-microphone recordings, is presented in [1].</p> <p> </p> <p> </p> <p><strong>Files included</strong></p> <p>The “audio” folder contains the audio files for each instrument in a given source: sourcenumber.wav, where “source” can be either violin, viola, cello, double bass, oboe, flute, clarinet, horn, trumpet or bassoon and “number” corresponds to the each separated instrument in a given source (e.g. there are two violins in the “mozart” piece, thus you will find “violin1.wav” and “violin2.wav” in the “mozart” folder).</p> <p> </p> <p>The “annotations” folder includes note onsets and offset annotations as MIDI and text files for the corresponding audio files in the dataset. The annotations are offered per source: source.txt and source.mid, where “source” can be either violin, viola, cello, double bass, oboe, flute, clarinet, horn, trumpet or bassoon. Additionally, for tasks as score-following, we provide MIDI which is not aligned with the audio as MIDI and text file: source_o.txt and source_o.mid. Furthermore, an additional MIDI file all.mid holds the tracks for all the sources in a single MIDI file.</p> <p>The text files comprise all the notes played by a source in the following format:</p> <p>Onset,Offset,Note name</p> <p>We recommend using the ground truth annotations from the text file as the MIDI might have problems due to the incorrect duration for some notes.</p> <p> </p> <p>The “Roomsim” folder contains the configuration files (“Text_setups”) and the impulse responses (“IRs”) which can be used with Roomsim[2] to generate the corresponding room configuration and the multi-microphone audio tracks used in our research.</p> <p>In the “Text_setups” folder, one can find the Roomsim text setups for the microphones: C,HRN,L,R,V1,V2,VL,WW_L,WW_R,TR.</p> <p>The “IRs” folder contains two subfolders: “conf1” can be used to generate the recordings for the Mozart piece, and “conf2” for the Bruckner, Beethoven, and Mahler pieces. We provide IR “.mat” files for each of the pairs (“microphone”,”source”): microphone_Ssourcenumber.mat, where “microphone” is C,HRN,L,R,V1,V2,VL,WW_L,WW_R,TR, and “sourcenumber” is the number of the sources ordered as in this list: bassoon (1), cello(2), clarinet(3), double bass(4), flute(5), horn(6), viola(7), violin(8), oboe(9), trumpet(10). Please consider that the Mozart piece does not contain oboe nor trumpet.</p> <p> </p> <p> </p> <p><strong>Conditions of Use</strong></p> <p>The annotations and the Roomsim configuration files in the PHENICX-Anechoic dataset are offered free of charge for non-commercial use only. You can not redistribute them nor modify them. Dataset by Marius Miron, Julio Carabias-Orti, Juan Jose Bosch, Emilia Gómez and Jordi Janer, Music Technology Group - Universitat Pompeu Fabra (Barcelona). This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.</p> <p>For the intellectual rights and the distribution policy of the audio recordings in this dataset contact Aalto University, Jukka Pätynen and Tapio Lokki. For more information about the original anechoic recordings we refer to the web page and the associated publication [2].</p> <p> </p> <p>Please Acknowledge PHENICX-Anechoic in Academic Research</p> <p>When the present dataset is used for academic research, we would highly appreciate if scientific publications of works partly based on the PHENICX-Anechoic dataset quote the following publications:</p> <p> </p> <p>M. Miron, J. Carabias-Orti, J. J. Bosch, E. Gómez and J. Janer, "Score-informed source separation for multi-channel orchestral recordings", Journal of Electrical and Computer Engineering (2016)</p> <p> </p> <p>Pätynen, J., Pulkki, V., and Lokki, T., "Anechoic recording system for symphony orchestra," <em>Acta Acustica united with Acustica</em>, vol. 94, nr. 6, pp. 856-865, November/December 2008.</p> <p> </p> <p><strong>Download</strong></p> <p>Dataset available</p> <p>Go to our download page.</p> <p> </p> <p><strong>Feedback</strong></p> <p>Problems, positive feedback, negative feedback, help to improve the annotations... it is all welcome! Send your feedback to: marius.miron@upf.edu AND mtg@upf.edu</p> <p>In case of a problem report please include as many details as possible.</p> <p> </p> <p><strong>References</strong></p> <p>[1] M. Miron, J. Carabias-Orti, J. J. Bosch, E. Gómez and J. Janer, "Score-informed source separation for multi-channel orchestral recordings", Journal of Electrical and Computer Engineering (2016)</p> <p>[2] Pätynen, J., Pulkki, V., and Lokki, T., "Anechoic recording system for symphony orchestra," <em>Acta Acustica united with Acustica</em>, vol. 94, nr. 6, pp. 856-865, November/December 2008.</p> <p>[2] Campbell, D., K. Palomaki, and G. Brown. "A Matlab simulation of" shoebox" room acoustics for use in research and teaching." <em>Computing and Information Systems</em> 9.3 (2005): 48.</p> <p> </p> <p> </p>
Binaural Impulse Response Dataset: Square Plate in Anechoic Chamber
<p><span>The dataset at hand contains impulse responses that have been measured along two discretized trajectories in the vicinity of a 25 mm thick 1 m x 1 m medium density fiberboard plate. Such data can serve as reference for the modelling of acoustic edge diffraction. This dataset was used in the context of research on binaural perception of diffracted sound in a publication that is in press at the Journal of the Acoustical Society of America</span></p>
Snapshot of anechoic data from OpenAIRlib.net, 26th February 2018
<p>This is a partial copy of the contents of "Anechoic Audio Database" found at http://www.openairlib.net/anechoicdb, on 26th of February 2018.</p> <p>The files provided are originally licensed with Creative Commons BY-SA or BY-NC. </p> <p>I do not retain any copyright on the files. All attribution is given to www.openairlib.net.</p> <p>This is just a convenience packaging for easy download of the material for academic purposes.</p>
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>
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>
NIGENS anechoic earsignals
<p><em><strong>N</strong>eural <strong>I</strong>nformation processing group <strong>GEN</strong>eral <strong>S</strong>ounds</em> database <strong>earsignals </strong>in different scene setups, simulated using KEMAR HRTFs.</p> <p>A list of all sounds is enclosed. These sounds have been processed with the Binaural Simulator of the Two!Ears System (https://github.com/TWOEARS), using KEMAR head HRTFs [1] to produce the following scenes:</p> <p>Distance head -- source: 3m<br> Azimuths (with respect to nose): 0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, 180°, 202.5°, 225°, 247.5°, 270°, 292.5°, 315°, 337.5°</p> <p><br> [1] Wierstorf, Hagen, Matthias Geier, and Sascha Spors. "A free database of head related impulse response measurements in the horizontal plane with multiple distances." Audio Engineering Society Convention 130. Audio Engineering Society, 2011.</p>
Validation Videos - Robotic System for Reproducible Mobile Networking Experimentation in Anechoic Chambers (Master Thesis)
<p><strong>Note on Robot's Referential:</strong></p> <p>The robot's referential can be inferred in the recording via the "Safety Position." The safety position is the same for both the Digital Model (Gazebo) and the Real Robot (Joint Position = [0.0, -1.57, 1.57, 0.0, 0.0, 0.0]).</p> <p>In the safety position, the robot is approximately aligned with the X-axis, with its end-effector on the positive side of the axis. The end-effector faces perpendicular to the Y-axis. The positive Z-axis points upwards towards the ceiling.</p> <p> </p>
PHENICX-Anechoic: note annotations for Aalto anechoic orchestral database
<p>This dataset includes audio and annotations useful for tasks as score-informed source separation, score following, multi-pitch estimation, transcription or instrument detection, in the context of symphonic music.</p> <p>This dataset was presented and used in the evaluation of:</p> <blockquote> <p>M. Miron, J. Carabias-Orti, J. J. Bosch, E. Gómez and J. Janer, "Score-informed source separation for multi-channel orchestral recordings", Journal of Electrical and Computer Engineering (2016))"</p> </blockquote> <p>On this web page we do not provide the original audio files, which can be found at the <a href="http://research.cs.aalto.fi/acoustics/virtual-acoustics/research/acoustic-measurement-and-analysis/85-anechoic-recordings.html">web page</a> hosted by Aalto University. However, with their permission we distribute the denoised versions for some of the anechoic orchestral recordings:</p> <blockquote> <p>Pätynen, J., Pulkki, V., and Lokki, T., "Anechoic recording system for symphony orchestra," Acta Acustica united with Acustica, vol. 94, nr. 6, pp. 856-865, November/December 2008.</p> </blockquote> <p>For the intellectual rights and the distribution policy of the audio recordings in this dataset contact Aalto University, Jukka Pätynen and Tapio Lokki. For more information about the original anechoic recordings we refer to the <a href="http://research.cs.aalto.fi/acoustics/virtual-acoustics/research/acoustic-measurement-and-analysis/85-anechoic-recordings.html">web page</a> and the associated publication [2]</p> <p>We provide the associated musical note onset and offset annotations, and the Roomsim[3] configuration files used to generate the <a href="http://repovizz.upf.edu/phenicx/anechoic_multi/">multi-microphone recordings</a> [1].</p> <p>The anechoic dataset in [2] consists of four passages of symphonic music from the Classical and Romantic periods. This work presented a set of anechoic recordings for each of the instruments, which were then synchronized between them so that they could later be combined to a mix of the orchestra. In order to keep the evaluation setup consistent between the four pieces, we selected the following instruments: violin, viola, cello, double bass, oboe, flute, clarinet, horn, trumpet and bassoon.</p> <p>We created a ground truth score, by hand annotating the notes played by the instruments. The annotation process involved gathering the original scores in MIDI format, performing an initial automatic audio-to-score alignment, then manually aligning each instrument track separately with the guidance of a monophonic pitch estimation.</p> <p>During the recording process detailed in [2], the gain of the microphone amplifiers was fixed to the same value for the whole process, which reduced the dynamic range of the recordings of the quieter instruments. This lead to problems with which we had to deal, in order to reduce the noise. In the paper we described the score-informed denoising procedure we applied to each track.</p> <p>A complete description of the dataset and the creation methodology, including the generation of the <a href="http://repovizz.upf.edu/phenicx/anechoic_multi/">multi-microphone recordings</a>, is presented in [1].</p> <p>Please Acknowledge PHENICX-Anechoic in Academic Research</p> <p><strong>Using this dataset</strong></p> <p>When the present dataset is used for academic research, we would highly appreciate if scientific publications of works partly based on the PHENICX-Anechoic dataset quote the publications above.</p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p> </p> <p><a href="https://www.upf.edu/web/mtg/phenicx-anechoic">https://www.upf.edu/web/mtg/phenicx-anechoic</a></p>
Loudspeaker array recordings in small anechoic chamber with two reflectors
<p>No description provided.</p>
TUT Sound Events 2018 - Ambisonic, Anechoic and Synthetic Impulse Response Dataset
<p><strong>Tampere University of Technology (TUT) Sound Events 2018 - Ambisonic, Anechoic, and Synthetic Impulse Response Dataset </strong></p> <p>This dataset consists of simulated anechoic first order Ambisonic (FOA) format recordings with stationary point sources each associated with a spatial coordinate. The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), spatial location in azimuth and elevation angles (in degrees), and distance from the microphone (in meters).</p> <p>The isolated sound events were taken from the <a href="https://archive.org/details/dcase2016_task2_train_dev">DCASE 2016 task 2 dataset.</a> This dataset consists of 11 sound event classes such as Clearing throat, Coughing, Door knock, Door slam, Drawer, Human laughter, Keyboard, Keys (put on a table), Page turning, Phone ringing and Speech. The sound events are randomly placed in a spatial grid with 10-degree resolution in full azimuth and [-60 60) degree elevation angles. Additionally, the sound events are placed at a random distance of [1 10] meters from the microphone.</p> <p>The license of the dataset can be found in the LICENSE file. The rest of the nine zip files consists of datasets for a given split and overlap. For example, the ov3_split1.zip file consists of the audio and metadata folders for the case of maximum three temporally overlapping sound events (ov3) and the first cross-validation split (split1). Within each audio/metadata folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Sound event localization and detection of overlapping sources using convolutional recurrent neural network</a>' work.</p>
TUT Sound Events 2018 - Circular array, Anechoic and Synthetic Impulse Response Dataset
<p><strong>Tampere University of Technology (TUT) Sound Events 2018 - Circular array, Anechoic and Synthetic Impulse Response Dataset</strong></p> <p>This dataset consists of simulated anechoic circular-array format recordings with stationary point sources each associated with a spatial coordinate. The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), spatial location in azimuth and elevation angles (in degrees), and distance from the microphone (in meters).</p> <p>The isolated sound events were taken from the <a href="https://archive.org/details/dcase2016_task2_train_dev">DCASE 2016 task 2 dataset.</a> This dataset consists of 11 sound event classes such as Clearing throat, Coughing, Door knock, Door slam, Drawer, Human laughter, Keyboard, Keys (put on a table), Page turning, Phone ringing and Speech. The sound events are randomly placed in a spatial grid with 10-degree resolution in full azimuth and [-60 60) degree elevation angles. Additionally, the sound events are placed at a random distance of [1 10] meters from the microphone.</p> <p>The license of the dataset can be found in the LICENSE file. The rest of the nine zip files consists of datasets for a given split and overlap. For example, the ov3_split1.zip file consists of the audio and metadata folders for the case of maximum three temporally overlapping sound events (ov3) and the first cross-validation split (split1). Within each audio/metadata folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Sound event localization and detection of overlapping sources using convolutional recurrent neural network</a>' work.</p>
TAU Moving Sound Events 2019 - Ambisonic, Anechoic, Synthetic IR and Moving Source Dataset
<p><strong>Tampere University (TAU) Moving Sound Events 2019 - Ambisonic, Anechoic and Synthetic Impulse Response (IR) and Moving Source Dataset</strong></p> <p>This dataset consists of simulated anechoic first order Ambisonic (FOA) format recordings with moving point sources each in 2D spherical space represented with azimuth and elevation angles. The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), starting spatial location and directional spatial location in azimuth and elevation angles (in degrees), angular velocity of motion, and distance from the microphone (in meters).</p> <p>The isolated sound events were taken from the DCASE 2016 task 2 dataset. This dataset consists of 11 sound event classes such as Clearing throat, Coughing, Door knock, Door slam, Drawer, Human laughter, Keyboard, Keys (put on a table), Page turning, Phone ringing and Speech. Every event is assigned a spatial trajectory on an arc with a constant distance from the microphone (in the range 1-10 m) and moving with a constant angular velocity for its duration. Due to the choice of the ambisonic spatial recording format, the steering vectors for a plane wave source or point source in the far field are frequency-independent. Hence, there is no need for a time-variant convolution or impulse response interpolation scheme as the source is moving; the spatial encoding of the monophonic signal was done sample-by-sample using instantaneous ambisonic encoding vectors for the respective DOA of the moving source. The synthesized trajectories in the dataset vary in both azimuth and elevation and are simulated to have a constant angular velocity in the range [-90, 90]/s with 10-degree/s steps.</p> <p>The license of the dataset can be found in the LICENSE file. The rest of the nine zip files consists of datasets for a given split and overlap. For example, the ov3_split1.zip file consists of the audio and metadata folders for the case of maximum three temporally overlapping sound events (ov3) and the first cross-validation split (split1). Within each audio/metadata folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Localization, Detection and Tracking of Multiple Moving Sound Sources with Convolutional Recurrent Neural Networks'</a> work.</p>
Anechoic recordings of Italian opera
<p>Multi-track recording of three opera excerpts:<br> G.Donizetti: “Come Paride vezzoso” from Elisir d'amore (Baritone, Choir and orchestra)<br> G. Verdi: “Di tale amor, che dirsi” from Il Trovatore (Soprano and orchestra)<br> G. Puccini: “Oh Mio Babbino Caro” from Gianni Schicchi (Soprano and orchestra)</p> <p> </p>
ARTSOUNDSCAPES PROJECT - REPOSITORY OF ANECHOIC SOUNDS
<p>This dataset was organised by members of the Artsoundscapes project. It serves as a library of anechoic music and sounds created by the Artsoundscapes project. It includes musical pieces of a great variety of genres and styles to nourish the different research lines. The Venezuelan musician and instrument maker, Yonder Rodriguez, played diverse membranophones and several reconstructions of archaeological instruments, such as Palaeolithic bone pipes, whistles and bullroarers, diverse kinds of rattles and scrapers, and traditional and shamanic Amazonian instruments. Catalan traditional music and other foreign traditions, such as Altaian traditional music, were performed by Guillem Codern, a Catalan musician who is well versed in traditional Tuvan music, such as khoomei and other vocal styles. Aditionally, singers María Illa (female voice) and Julen Gerrikabeitia (male voice), performed a selection of contemporary the pop-rock pieces. More detail about the sound excerpts selected for the stimulus set is provided in the ARTSS-Anechoic recording list (excel file).</p> <p>Data collection was organized by Samantha López-Mochales and Raquel Jiménez-Pasalodos, assisted by Lidia Alvarez-Morales and Margarita Díaz-Andreu. All the sounds were recorded in uncompressed mono format in the anechoic chamber at the La Salle Acoustics Laboratory (Ramon Llull University, Barcelona, Spain) in July 2021. Dr Marc Arnela, Carme Julia Martínez Suquia and Raquel Aparicio-Terres helped during the recording sessions.</p> <p>For the intellectual rights and the distribution policy of the audio recordings in this dataset contact Dr Margarita Díaz-Andreu. If you use our dataset, please email us at <a href="mailto:artsoundscapesproject@gmail.com">artsoundscapesproject@gmail.com</a> and tell us about your research.</p>
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.