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18 results for “sound source”
Sound samples for the evaluation of source-level blending between violins
<p>The repository includes monophonically rendered sound files used in the source-level blending evaluation. The sound samples provided are recorded from a violin ensemble performance at Detmold Concert House as a part of an investigation on the influence of acoustic environment on the impression of blending [1]. DPA 4099 clip-on microphones were used to capture individual violins in the performance.</p> <p>Each sound sample consists of two violin signals that were rendered by downmixing to a monophonic format at 44.1kHz/16-bit depth. The impression of blending between the two violins in each sample was rated by a group of trained listeners, and the results are provided in the file, 'Sound sample_description'. Please refer to the publication for more details on the performance of the violin ensemble. Also, please cite the publication if these samples are used for scientific evaluations.</p> <p>In addition to the sound samples, three Matlab figures that demonstrate the cluster distribution of MFCC features extracted from sound samples (that are classified into blended and non-blended classes) transformed using Principal component Analysis (PCA), Linear Discriminant Analysis (LDA), and t-Stochastic Neighbourhood Embedding (t-SNE), are included for 3D visualization. This corresponds to Figure 9 in [2].</p> <p> </p> <p> [1] Jithin Thilakan and Malte Kob, “Evaluation of subjective impression of instrument blending in a string ensemble”, Fortschritte der Akustik - DAGA 2021 in Wien, pp. 524-527.</p> <p>[2] Jithin Thilakan, Balamurali BT, Jer-Ming Chen, Malte Kob, “Classification of the perceptual impression of source-level blending between violins in a joint performance”, currently under review at Acta Acustica.</p>
Source Data for Manuscript "Sodium salicylate improves detection of amplitude-modulated sound in mice"
<p>This repository contains the source data for our papers <strong>Sodium salicylate improves detection of amplitude-modulated sound in mice </strong>(van den Berg*, Wong*, Houtak, Williamson, Borst). The code to generate figure panels can be found in our github repository at https://github.com/aaronbwong/salicylateonam</p>
SPASS dataset: A synthetic polyphonic dataset with spatiotemporal labels of sound sources
<p>SPASS is a synthetic dataset that consists of 10-seconds audio segments from 5 acoustic scenes:</p> <ul> <li>Park</li> <li>Square</li> <li>Street</li> <li>Waterfront</li> <li>Market</li> </ul> <p>Each acoustic scene has 5,000 audio recordings and its corresponding metadata.</p> <p>The audio recordings were created using a 3D acoustic simulation environment (RAVEN, <a href="https://www.virtualacoustics.org/RAVEN/">https://www.virtualacoustics.org/RAVEN/</a>).</p> <p>SPASS was made as a training dataset for the FuSA system (<a href="https://www.acusticauach.cl/fusa/">https://www.acusticauach.cl/fusa/</a>). This is a polyphonic dataset for Sound Event Detection (SED) tasks.</p> <p>The metadata files includes the class of each sound event, their onset and offset in time, the position in the space (cartesian) and their final position if the class was moving.</p> <p>This research was funded by ANID FONDEF grant number ID20I10333.</p>
Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.
<p>supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146</p>
Dataset for sound source localization with 101 Blinky sound-to-light conversion sensors
<p>Blinkies are sound-to-light conversion devices that can be used to monitor the sound level over large areas. The data from the sensors is harvested using a video camera. This dataset contains seven videos that were recorded in the gymnastical hall of Tokyo Metropolitan University, Hino Campus on July 3rd 2018. In the video, 101 Blinkies are spread on the ground of the gymnastic hall. A bluetooth speaker mounted on a remote controlled car runs between the Blinkies, causing them to change intensity. The file `pyramic_json` is a JSON format file containing all the meta-data necessary such as sensor locations, room dimensions, and segmentation information.</p> <p>This dataset was used to demonstrate sound source localization in the paper "Blinkies: Open source sound-to-light conversion sensors for large-scale acoustic sensing and applications" by Robin Scheibler and Nobutaka Ono (to appear).</p>
Monthly nitrogen point-source loading dataset for the Long Island Sound Watershed
<p>The attached dataset and code accompanies a Data in Brief article, "Monthly nitrogen point-source loading dataset for the Long Island Sound Watershed."</p> <p>Abstract: Quantifying point source nitrogen loads to the Long Island Sound watershed is important because they contribute to the annual occurrence of hypoxia in Long Island Sound. However, the data are not easily accessible and are not available at a central location, making it difficult to characterize the magnitude of loading, seasonal patterns and long-term trends. For the period between April 1989 and September 2021, we gathered all available monthly nitrogen data for wastewater treatment plants within the Long Island Sound watershed from the U.S. Environmental Protection Agency Integrated Compliance Information System-National Pollutant Discharge Elimination System (ICIS-NPDES) and Permit Compliance System, and Connecticut Department of Energy and Environmental Protection databases. Data were checked for quality assurance and unreasonable outliers were imputed with means of other observations within the same year and season. Estimates were also imputed for missing observations. We publish both raw data with missing observations and a curated dataset with imputed estimates for missing observations. Monthly point source nitrogen load data can be used for water quality modeling and to infer, by subtraction, the relative contribution of nonpoint sources to gauged watershed loads. Additionally, the scripts used to reformat data from the ICIS could be repurposed to collect similar data in other regions.</p>
CAPTDURE: Captioned Sound Dataset of Single Sources
<p><strong>Description</strong></p> <p>This is a dataset with captions for a single-source sound that can be used in various tasks that use environmental sounds. The dataset consists of 1,044 single-source sounds and 4,902 captions (3 or more captions per single-source sound). This dataset also consists of 1,044 multiple-source sounds and 3,132 captions (3 captions per multiple-source sound). The detail of the dataset is described in [1].</p> <p><strong>Conditions of use</strong></p> <p>This dataset was made by <strong>Hitachi, Ltd.</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.</p> <p><strong>Citation</strong></p> <p>If you use this dataset, please cite as follow:</p> <p>[1] Yuki Okamoto, Kanta Shimonishi, Keisuke Imoto, Kota Dohi, Shota Horiguchi, and Yohei Kawaguchi, "CAPTDURE: Captioned sound Dataset of Single Sources," Proc. INTERSPEECH, pp. 1683-1687, 2023.</p> <p><strong>Feedback</strong></p> <p>If there is any problem, please contact us</p> <ul> <li>Yuki Okamoto, <a href="mailto:y-okamoto@ieee.org">y-okamoto@ieee.org</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> </ul>
Database of Spherical Harmonic Representations of Sound Source Directivities
<p>This is a database of complete spherical harmonic representations of the directivities of sound sources. The data are provided as impulse responses that represent the directivity of the given source in a given discrete direction. The Matlab script <code>compute_spherical_harmonics_model.m</code> demonstrates how a spherical harmonic representation can be computed from the data. We do not provide spherical harmonic coefficients directly because of the multitude of definitions of spherical harmonics and also of the Discrete Fourier transform. We rather ask you to select the combination of definitions you would like to use and compute the spherical harmonic coefficients on demand. You may want to add re-sampling or zero padding and the like to make the data compatible with your intended application.</p> <p>As of now, all spherical harmonic representations are based on previously published data. Please do not forget to site this repository as well as the original repositories when using the data. References to the original sources are provided with each dataset. All data are bandlimited to the spherical harmonic order <code>N</code> that is specified in the corresponding file name. The conversion between raw data and spherical harmonic coefficients is therefore essentially lossless.</p>
Data from "Asymmetric visual capture of virtual sound sources in the distance dimension"
<p>This repository will contain raw and processed data used and described in:</p> <p><strong>Zahorik P (2022) Asymmetric visual capture of virtual sound sources in the distance dimension. Front. Neurosci. 16:958577. doi: 10.3389/fnins.2022.958577</strong></p>
Dataset of Room Impulse Responses from Baffled Microphone Arrays and Sound Sources at Three Elevations
<p>This data set contains a collection of impulse responses (stored in SOFA format) from <em>spherical microphone arrays</em> (<strong>SMA</strong>s), <em>equatorial microphone arrays</em> (<strong>EMA</strong>s), and <em>non-spherical microphone arrays</em> (<strong>XMA</strong>s). Thereby, impulse response sets are provided for each array type at various spatial resolutions, for a loudspeaker sound source at three source elevations, and in four diverse acoustic environments (see <strong>DATA</strong> section for a full description).</p> <p>The original purpose of the microphone array data is the binaural rendering in the <em>spherical harmonics</em> (<strong>SH</strong>) domain into ear signals for high-fidelity reproduction of the acoustic scenario via headphones. Therefore, <em>binaural room impulse responses</em> (<strong>BRIR</strong>s) for 360 horizontal head orientations of a <em>G.R.A.S KEMAR</em> acoustic dummy head are provided as a reference for each scenario.</p> <p>Please contact the authors for questions or additional information regarding the room setups and utilized measurement devices.</p> <p> </p> <p><strong>======<br> DATA<br>======</strong></p> <p>This archive contains the processed impulse response sets of various measurement configurations, as described in this section.</p> <p>Directory "resources/ARIR_processed/":</p> <ul> <li>Post-processed SMA and EMA impulse responses <ul> <li><strong>"_SMA*_"</strong> or <strong>"_EMA*_"</strong> in the file name</li> <li>In SOFA format with <em>"SingleRoomSRIR"</em> convention</li> <li>From 1x <em>DPA 4060</em> microphone flush mounted in a wooden spherical scattering body with an 8.5 cm radius</li> <li>High-resolution data (measured sequentially on VariSphear turntable with two degrees-of-freedom rotations): <ul> <li>Hall: <strong>1202</strong> <strong>channels</strong> (Lebedev grid) for maximum SH order 29</li> <li>Others: <strong>2702</strong> <strong>channels</strong> (Lebedev grid) for maximum SH order 44</li> </ul> </li> <li>Lower-resolution data via subsampling in the SH domain (arbitrary sampling grids and lower target orders can be achieved): <ul> <li>SH order 29: <strong>1742 channels</strong> (t-design grid) for SMA; <strong>59</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 12: <strong>314</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>25</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 8: <strong>146</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>17</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 4: <strong>42</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>9</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 2: <strong>14</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>5</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 1: <strong>6</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>3</strong> <strong>channels</strong> (equiangular grid) for EMA</li> </ul> </li> </ul> </li> <li>Post-processed XMA impulse responses <ul> <li><strong>"_XMA*_"</strong> in the file name</li> <li>In SOFA format with <em>"SingleRoomSRIR"</em> convention</li> <li>From 18x <em>Rode Lavalier GO</em> microphone mounted in an elastic band on a wooden head-shaped scattering body (7.5 cm to 10.5 cm radius)</li> <li>High-resolution data (measured simultaneously): <ul> <li><strong>18 channels</strong> for maximum SH order 8</li> </ul> </li> <li>Lower-resolution data via integer subsets of microphones: <ul> <li>SH order 4: <strong>9 channels</strong></li> <li>SH order 2: <strong>6 channels</strong></li> </ul> </li> <li>Anechoic: For 360 horizontal scattering body orientations (measured sequentially on a VariSphear turntable with azimuth in 1-degree steps)</li> <li>Rooms: For 36 horizontal scattering body orientations (measured sequentially on VariSphear turntable with azimuth in 10-degree steps)</li> </ul> </li> <li>Generated XMA calibration filters and equalization filters <ul> <li><strong>"_x_nm_"</strong> and <strong>"_e_nm_"</strong> in the file name</li> <li>In proprietary Matlab format</li> <li>Time-domain representation of filters in the respective orders of "real" spherical harmonics</li> </ul> </li> <li>Post-processed binaural impulse responses <ul> <li><strong>"_KEMAR_"</strong> in the file name</li> <li>In SOFA format with <em>"SingleRoomSRIR"</em> convention</li> <li>From <em>G.R.A.S KEMAR</em> dummy head with large pinna</li> <li>For 360 horizontal head orientations (measured sequentially on VariSphear turntable with azimuth in 1-degree steps)</li> </ul> </li> <li>Thereby, impulse response sets are included for five acoustic environments <ul> <li><strong>"Simulation_"</strong>: Anechoic simulation of a plane wave impinging from the frontal direction on the array (SMA and EMA only)</li> <li><strong>"Anechoic_"</strong>: Anechoic measurement of a <em>Genelec 8030A</em> loudspeaker at the same height of the array</li> <li>"<strong>LabDry_"</strong>: Room measurement in an acoustically damped laboratory of a <em>Genelec 8030A</em> loudspeaker at three different source heights (the direct floor reflection is attenuated with an additional porous absorber but otherwise identical to the following condition)</li> <li><strong>"LabWet_"</strong>: Room measurement in an acoustically damped laboratory of a <em>Genelec 8030A</em> loudspeaker at three different source heights (the direct reflection is not obstructed from the hard concrete floor, but otherwise identical to the former condition)</li> <li><strong>"Hall_"</strong>: Room measurement in a very reverberant hall of a <em>Genelec 8030A</em> loudspeaker at three different source heights</li> </ul> </li> <li>Thereby, the room impulse response sets are included for three relative source elevations (from placing the loudspeaker to varying heights on the same vertical axis) <ul> <li><strong>"_SrcHigh"</strong>: The source is located above the horizon of the receiver</li> <li>"<strong>_SrcEar"</strong>: The source and receiver are located at the same height</li> <li><strong>"_SrcLow"</strong>: The source is located below the horizon of the receiver</li> </ul> </li> <li>Additionally, anechoic impulse responses of the measurement loudspeaker and the utilized microphones are included <ul> <li><strong>"Anechoic_MicSMAnoTape_"</strong>: SMA measurement microphone without the applied tape (the source was compensated)</li> <li><strong>"Anechoic_MicSMAwithTape_"</strong>: SMA measurement microphone with the applied tape (the source was compensated)</li> <li><strong>"Anechoic_MicXMAmic19_"</strong>: XMA measurement microphone (the source was compensated)</li> <li><strong>"Anechoic_SrcFreeField_"</strong>: Measurement source (on-axis) (the influence of the utilized high-quality free-field measurement microphone can be neglected)</li> <li><strong>"Anechoic_SrcFreeField+MicSMAnoTape_"</strong>: Measurement source and SMA measurement microphone without the tape applied</li> <li><strong>"Anechoic_SrcFreeField+MicSMAwithTape_"</strong>: Measurement source and SMA measurement microphone with the tape applied</li> <li><strong>"Anechoic_SrcFreeField+MicXMAmic19_"</strong>: Measurement source and XMA measurement microphone</li> <li>Overall, the resulting impulse response sets contain the following compensations (including exact compensation of the phase/time behavior): <ul> <li>Anechoic KEMAR: Source</li> <li>Anechoic SMA/EMA/XMA: Source and array microphones</li> <li>Rooms KEMAR: None</li> <li>Rooms SMA/EMA/XMA: Array microphones</li> <li>There is the option to compensate for the source's on-axis response in the room measurement data. However, the direction-dependent directivity of the loudspeaker cannot be compensated. Therefore, we decided not to compensate for the source in the room measurement data since the on-axis frequency response of the utilized loudspeaker is reasonably flat.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p><strong>===========<br> DATA_RAW<br>===========</strong></p> <p><strong>This archive is too large to be uploaded to Zotero (around 77.5 GB). Please get in touch with the authors to request the data.</strong></p> <p>The archive contains the raw acoustic data of all measurement configurations captured by the measurement scripts (see section <strong>CODE_AND_PLOTS</strong>). The data yields the final impulse responses (see section <strong>DATA</strong>), as described in this section.</p> <p>Directory "resources/ARIR_raw/":</p> <ul> <li>Subdirectories by room and source position containing the raw SMA, XMA, and KEMAR acoustic measurement data</li> <li>In proprietary Matlab format, separate for every measurement position of each configuration</li> <li>Each data file contains extensive metadata, e.g., describing the utilized hardware devices, input/output ports, and descriptions.</li> <li>Each data file contains the raw utilized exponential sweep signal and the resulting captured microphone signals. Each impulse response may be recomputed with alternative deconvolution and post-processing parameters.</li> </ul> <p>Directory "resources/ARIR_raw/Logs_temp_humidity/":</p> <ul> <li>Air temperature and humidity data were captured in 5-second intervals during all acoustic measurements</li> <li>In CSV format (automatically loaded and included in the final impulse response sets as part of the measurement post-processing; see section <strong>CODE_AND_PLOTS</strong>)</li> <li>This data is not further utilized at the moment but seemed worthwhile to capture since some acoustic measurements (particularly the high-resolution SMA data sets) were conducted over multiple hours.</li> </ul> <p> </p> <p><strong>==================<br> CODE_AND_PLOTS<br>==================</strong></p> <p>This archive contains the code required to gather the raw acoustic measurement data (see section <strong>DATA_RAW</strong>), the code to post-process and yield the final impulse response data (see section <strong>DATA</strong>), and the resulting plots as described in this section.</p> <p>Directory "dependencies/":</p> <ul> <li>Matlab and Python functions that are utilized in the code</li> <li>Additional dependencies of available open-source projects may be required for certain code functions. If so, the source and setup process for the necessary dependencies are documented in the code header.</li> </ul> <p>Directory "plots/":</p> <ul> <li>Plots that were exported (and that may be regenerated) by the following scripts to validate different stages of the data simulation, measurement, and subsampling.</li> </ul> <p>Shell script "x1_Start_Jupyter.sh":</p> <ul> <li>Prepare a Python environment with the required tools described as dependencies.</li> <li>Activate the prepared Python environment to perform impulse response measurements using Jupyter Notebooks setup for different acoustic settings.</li> </ul> <p>Python Jupyter notebook "x1a_Measure_Microphones.ipynb":</p> <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Perform a series of acoustic measurements of all utilized microphones in an anechoic environment.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> <p>Python Jupyter notebook "x1b_Measure_BRIRs.ipynb":</p> <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Generate a horizontal grid of measurement orientations for the VariSphear turntable according to the desired dummy head orientations.</li> <li>Perform a series of acoustic measurements of the dummy head at the pre-defined grid in anechoic and various room environments.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> <p>Python Jupyter notebook "x1c_Measure_SMAs.ipynb":</p> <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Generate a spherical grid of measurement orientations for the VariSphear turntable according to the desired SMA sampling grid.</li> <li>Perform a series of acoustic measurements of the SMA microphone at the pre-defined grid in anechoic and various room environments.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> <p>Python Jupyter notebook "x1d_Measure_XMAs.ipynb":</p> <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Generate a horizontal grid of measurement orientations for the VariSphear turntable according to the desired scattering body orientations.</li> <li>Perform a series of acoustic measurements of the XMA microphones at the pre-defined grid in anechoic and various room environments.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> <p>Matlab script "x1e_Simulate_SMAs.m":</p> <ul> <li>Simulate a plane wave impinging from an arbitrary direction on SMAs and EMAs with a desired sampling grid in an anechoic environment.</li> <li>The simulations are helpful to evaluate the rendering method and to investigate the influence of different sampling grids and equalization methods on the rendered binaural signals.</li> </ul> <p>Matlab script "x2_Gather_And_Plot_Measurements.m":</p> <ul> <li>Gather the stored single files with individually measured impulse responses and the according metadata into a combined data set.</li> <li>The initial impulse responses can be recomputed with pre- and post-processing parameters tuned towards the specific acoustic scenario, including compensation of provided source and receiver impulse responses.</li> <li>Many plots may be generated during the processing to validate the input and output data.</li> </ul> <p>Matlab script "x2a_Compare_Measurement_Lengths.m":</p> <ul> <li>Compare the length of the resulting impulse responses of designated measurement configurations.</li> <li>This may be helpful for the tuning of pre-processing and post-processing parameters of the measured impulse responses.</li> </ul> <p>Matlab script "x3_Subsample_Measurements.m":</p> <ul> <li>Spatially subsample a high-resolution directional impulse response data set into a different (lower-resolution) sampling grid in the spherical harmonics domain.</li> <li>This is suitable for array and HRIR data sets.</li> <li>The script also compares the subsampled data against a reference set if available. In the current data set, an evaluation is performed for an anechoic simulation and a room measurement of an SMA at SH order 8.</li> </ul> <p>Matlab script "x3a_Gather_XMA_Measurements.m":</p> <ul> <li> <p>Transform anechoic XMA measurement data from SOFA into the data format required by the processing scripts to calculate the respective calibration and equalization filters.</p> </li> </ul> <p>Readme file "x3b_Generate_XMA_Filters.txt":</p> <ul> <li>The code for this functionality follows the publication [1] but is currently not polished enough for publication. Please contact Jens Ahrens (jens.ahrens@chalmers.se) for questions regarding this functionality.</li> <li>[1] J. Ahrens, H. Helmholz, D. Lou Alon, and S. V. Amengual Garí, “Spherical Harmonic Decomposition of a Sound Field Using Microphones on a Circumferential Contour Around a Non-Spherical Baffle,” <em>IEEE/ACM Trans. Audio, Speech, Lang. Process.</em>, vol. 30, pp. 3110–3119, 2022, doi: 10.1109/TASLP.2022.3209940.</li> </ul> <p>Matlab script "x3c_Gather_XMA_Filters.m":</p> <ul> <li>Rename the files containing the computed calibration and equalization filters into a suitable convention for this collection of scripts.</li> <li>The generated name includes an incremental index to track different versions of provided filter sets.</li> </ul> <p>Matlab script "x3d_Compare_XMA_Filters.m":</p> <ul> <li> <p>Generate various time domain and frequency domain plots to compare different versions of the generated XMA calibration and equalization filters.</p> </li> </ul> <p> </p> <p><strong>================<br> DOCUMENTATION<br>================</strong></p> <p>This archive contains additional documentation of the setups and processes while conducting the acoustic measurements, as described in this section.</p> <p>Directory "documentation/":</p> <ul> <li>Various photographs of the different room, source, and receiver arrangements of the data set</li> <li>The room dimensions and source and receiver positions are documented in the form of the original measurement notes (this may be improved in the future).</li> </ul> <p> </p>
Sound source localization with varying amount of visual information in virtual reality [dataset]
<p>This is the dataset that belongs to the publication "Sound source localization with varying amount of visual information in virtual reality".</p> <p>-The file "AVIL_lab.fbx" contains the visual model of the loudspeaker environment that was used throughout the experiment.</p> <p>-The file "localization_task_instruction_final.docx" contains the information sheet that was handed to the subjects before the experiment.</p> <p>-The file "localizationData_public.xlsx" contains the responses from the subjects (column B). The responses in azimuth and elevation (columns F&G) are corrected for the pointing bias (columns H&I).</p>
Sound-source directivity dataset for four loudspekaers
<p>A dataset of sound-source directivity measurements for four different sound sources that can be used in acoustic measurements. </p> <p>The loudspeakers included:</p> <ul> <li>Genelec 8030B studio monitor;</li> <li>Genelec 8331A studio monitor;</li> <li>01 dB LS01 omnidirectional loudspeaker conforming with ISO 3382-1:2009 standard (OmniSource);</li> <li>Mixed-order spherical loudspeaker type 393 (information on design of the loudspeaker is found in the publication: https://www.researchgate.net/publication/333132335_Design_and_Control_of_Mixed-Order_Spherical_Loudspeaker_Arrays). <strong>NOTE: </strong>the orientation of the drivers is specified in the table below.</li> </ul> <table> <tbody> <tr> <td><strong>Elevation (deg.)</strong></td> <td><strong>Azimuth (deg.)</strong></td> <td><strong>Driver #</strong></td> </tr> <tr> <td>0</td> <td>0</td> <td>15</td> </tr> <tr> <td>0 </td> <td>-40 (320)</td> <td>11</td> </tr> <tr> <td>0 </td> <td>-80 (280)</td> <td>10</td> </tr> <tr> <td>0</td> <td>-120 (240)</td> <td>9</td> </tr> <tr> <td>0</td> <td>-160 (200)</td> <td>8</td> </tr> <tr> <td>0</td> <td>40</td> <td>7</td> </tr> <tr> <td>0</td> <td>80</td> <td>6</td> </tr> <tr> <td>0</td> <td>120 </td> <td>5</td> </tr> <tr> <td>0</td> <td>160</td> <td>4</td> </tr> <tr> <td>-45</td> <td>120</td> <td>13</td> </tr> <tr> <td>-45</td> <td>0</td> <td>12</td> </tr> <tr> <td>-45</td> <td>-120 (240)</td> <td>14</td> </tr> <tr> <td>45</td> <td>-60 (300)</td> <td>2</td> </tr> <tr> <td>45</td> <td>60</td> <td>1</td> </tr> <tr> <td>45</td> <td>180</td> <td>3</td> </tr> </tbody> </table> <p> </p> <p>The directivity was measured in the anechoic chamber <em>Lampio </em>at the Acoustics Lab of Aalto University in Espoo, Finland.</p> <p>The measurement microphones were G.R.A.S. 46AF 1/2" free-field microphones and B&K 4191 1/2" free-field microphone. All the receivers were fixed on a custom-made measurement arc. The sound sources were placed ona turntable allowing for high angular resolution in the XY-plane.</p> <p>More information about the measurements is available in the paper: <strong>Anthony Gallien, Karolina Prawda, and Sebastian J. Schlecht, "Matching early reflections of simulated and measured RIRs by applying sound-source directivity filters", in Proc. AES ASR 2024, 22-26 Jan. 2024, Le Mans, France</strong></p> <p>Directivity plots for all sound sources are available online: http://research.spa.aalto.fi/publications/papers/aes-asr24-recreate-RIRs/ </p> <p>The work leading to producing the dataset and related research was conducted as Anthony Gallien's intership project at Aalto University Acoustics Lab from May 22 -- August 18, 2023.</p> <p> </p> <h3>How to read this dataset:</h3> <p>The measurements of each sound-source are available as a <strong>Spatially Oriented Format for Acoustics (SOFA)</strong>, AES69-2015 file or as a <strong>set of individual directivity measurements in .wav format</strong> included in a .zip file with the name of a respective sound source. </p> <p>The naming convention for Genelec studio loudspeakers and 01 dB omnidirectional loudspeaker is:</p> <p><strong>Calibrated_IR_elevation_E_azimuth_A.wav</strong></p> <p>The naming convention for the mixed-order loudspeaker is:</p> <p><strong>Calibrated_IR_elevation_E_azimuth_A_driver_D.wav</strong></p> <p><strong>NOTE</strong> that directivity measurements for mixed-order loudspeaker and elevation above 0 degrees are missing due to a loudspeaker-related technical issue during the measurement!</p> <p> </p> <p>The azimuth and elevation angles are determined according to the scheme presented in the file <strong>azimuth_elevation.pdf</strong>.</p> <p>Where elevation is relative to XY-plane at the horizontal axis of the sound source and azimuth is relative to the vertical plane at the vertical axis of the loudspekaer. </p> <p>The elevation angles range from -80 degrees to 90 degrees in 10-degree steps. The azimuth angles range from 0 degrees to 359 degrees in 1-degree steps for Genelec loudspekers and omnidirectional sound source, and in 5-degree steps for the mixed-order loudspeakers. The azimuth angles increase clockwise relative to the axis of the loudspeaker (1 degree azimuth means 1 degree right of the loudspeaker axis, 359 degree azimuth means 1 degree left of the loudspeaker axis).</p> <p> </p> <p>The datset includes a total of 38 880 measurements (6 480 measurements for omnidirectional source and each of the Genelec loudspeakers, 19 440 measurements for the mixed-order loudspeaker).</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>
A large joint sound scene and sound event dataset for source separation of foreground sound events
<p>This large scale data set contains 10000 samples of sound scenes generated from real world recordings, and the original source recordings. It includes 10 different backgrounds with 6-9 appropriate foreground sound events. Strong labels (timed annotations) are provided in four formats for all samples. The original sourceids to identify the class type, a two source method to simply separate foreground and backgrounds, a 32 source annotation for all distinct foregrounds, and a by background (scene) type annotation where sources are according to the background. </p> <p>Baseline results will be presented later in 2020. Further evolutions of this dataset will also be produced with more complex, polyphonic foreground sound events. Please email h.bear@qmul.ac.uk with any questions.</p> <p>Data is free to use for Research purposes only. </p>
Automating the Detection of Marine Sound Sources
<p>This dataset contains the training and testing labelled datasets, along with the required scripts, to replicate the CNN model developed within this thesis. For access to raw acoustic dataset used throughout the thesis you should contact the author, it may be available upon request.</p>
Audio-visual sound source localization and separation
<p>CVPR 2021 tutorial</p>
Crocodiles use both interaural level differences and interauraltime differences to locate a sound source
<p>Dataset used in the study "Crocodiles use both interaural level differences and interaural time differences to locate a sound source".</p> <p>The 111 trajectories used in this study are included in this deposit.</p>
TAU Moving Sound Events 2019 - Ambisonic, Reverberant, Real-life IR and Moving Source Dataset
<p><strong>Tampere University (TAU) Moving Sound Events 2019 - Ambisonic, Reverberant and Real-life Impulse Response and Moving Source Dataset</strong></p> <p>This dataset consists of real-life first order Ambisonic (FOA) format recordings with moving point sources each in 2D spherical space represented with azimuth and elevation angles. The dataset was generated by collecting impulse responses (IR) from a real environment using the Eigenmike spherical microphone array. The measurement was done by slowly moving a Genelec G Two loudspeaker continuously playing<br> a maximum length sequence around the array in circular trajectory in one elevation at a time. The playback volume was set to be 30 dB greater than the ambient sound level. The recording was done in a corridor inside the university with classrooms around it during work hours. The IRs were collected at elevations −40 to 40 with 10-degree increments at 1 m from the Eigenmike and at elevations −20 to 20 with 10-degree increments at 2 m. </p> <p>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. All sound events in this dataset are moving only along azimuth with a constant angular velocity in the range [-90, 90]/s with 10-degree/s steps. 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 in azimuth and elevation angles (in degrees), the angular velocity of motion and distance from the microphone (in meters).</p> <p>The isolated sound events were taken from the urbansound8k dataset. This dataset consists of 10 sound event classes such as air_conditioner, car_horn, children_playing, dog_bark, drilling, enginge_idling, gun_shot, jackhammer, siren, and street_music. We do not consider air_conditioner and children_playing sound events. Further, we only include the sound event examples marked as foreground in the dataset. We used the splits 1, 8 and 9 provided in the urbansound8k as the three CV splits. These splits were chosen as they had a good number of examples for all the chosen sound event classes after selecting only the foreground examples. During the sound scene synthesis, every sound event is assigned a spatial trajectory on an arc with a constant distance from the microphone and moving with a constant angular velocity for its duration.</p> <p>Other than the license file, there are nine zip files that consist of the dataset and corresponding metadata for given split and overlap. For example, the ov3_split1.zip file consists of training and testing recordings and metadata for the case of a maximum of three temporally overlapping sound events (ov3) for the first cross-validation split (split1). Within each 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> <p>Data collector (s): Fagerlund, Eemi; Koskimies, Aino; Hakala, Aapo</p>
ScienceDex guides
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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.