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40 results for “Room Impulse Response”
Ambisonic Room Impulse Responses
<p>Datasets of Ambisonic room impulse responses from 3 european museums/touristic sites:</p> <ul> <li>La Fundació Miro, Barcelona, Spain</li> <li>Die Alte Pinakotheke, Munich, Germany</li> <li>St Andrews Castle, St Andrews, Scotland</li> </ul> <p>The datasets are in SOFA format of convention AmbisonicsDRIR, recently proposed by the author.</p>
Spatial Room Impulse Response Dataset: A Robot's Journey Through Coupled Rooms of a Reverberant University Building
<p>This is a dataset of Spatial Room Impulse Responses obtained by a robot equipped with a microphone array.</p> <p>The measurements were conducted in a reverberant university building, the <em>Helmholtz</em> building at<em> Technische Universität Ilmenau</em> (coordinates: N50.6815788133375°, E10.939294371903342°). All the floors in the building are covered with bare stone tiles, the walls are not acoustically treated. Only the hallway has a suspended acoustic ceiling. The file "Pictures Overview.jpg" shows some impressions of the building. Note that the floorplan only shows parts of the building that were connected to the measurement area by open doors.</p> <p>The area covered by the robot is in a hallway on the top floor (2nd floor starting with ground floor) with two stairwells at both ends. To specifically study the behavior of coupled rooms and occluded sources, the sound sources were placed in adjacent sections of the building and on multiple floors. See the file "Measurement Overview.jpg" for an overview of the source positions and the receiver areas covered. Areas 2 and 3 were captured with a higher spatial resolution than area 1 to analyze the transition between the hallway and the staircases. The receiver positions form a uniform grid, the pitch between positions is shown in the following table. Due to time and technical constraints, only a maximum of 3 sources were used per run, so there are not all combinations of sources and receiver areas. Refer to the following table to see which source was active for which area and which zip file contains the according data:</p> <table> <tbody> <tr> <th>Filename</th> <th>Sources</th> <th>Receiver Area</th> <th>Receiver Positions [ct]</th> <th>Pitch [cm]</th> </tr> </tbody> <tbody> <tr> <td>Helmholtzbau_OG2_HM_HS.zip</td> <td>HM, HS</td> <td>Area 1</td> <td>143</td> <td>50</td> </tr> <tr> <td>Helmholtzbau_OG2_SML_SSL_SSU.zip</td> <td>SML, SSL, SSU</td> <td>Area 1</td> <td>154</td> <td>50</td> </tr> <tr> <td>Helmholtzbau_OG2_SMU_SML_HM.zip</td> <td>SMU, SML, HM</td> <td>Area 2</td> <td>88</td> <td>25</td> </tr> <tr> <td>Helmholtzbau_OG2_SSU_SSL_HS.zip</td> <td>SSU, SSL, HS</td> <td>Area 3</td> <td>92</td> <td>25</td> </tr> </tbody> </table> <p>As an example "Plot Reverberation Time.jpg" shows the reverberation times for all measured positions of Area 1 and 2 with speaker HM.</p>
A High-Resolution Spatial Room Impulse Response Database
<p><strong>A High-Resolution Spatial Room Impulse Response Database:</strong></p> <p>A Database of various SRIRs measured in three rooms, for two receiver and 3 source positions each. The positions are shown in the floor plans. </p> <p><strong>Naming Convention:</strong></p> <p><strong>Receivers</strong>: SMA (DRIRs): spherical microphone array impulse responses on a 2702 sampling point Lebedev grid.</p> <p> KU100 (BRIRs): Neumann KU100 dummy head impulse responses on a 360 sampling point horizontal grid</p> <p> Omni_ir: Omnidirectional impulse responses measured with an Earthworks M30 microphone </p> <p><strong>Receiver positions:</strong> P1, P2 as indicated in the floor plans</p> <p><strong>Source positions:</strong> LSL: left speaker, LSR: right speaker, LSC: center speaker as indicated in the floor plans </p> <p><strong>Rooms: </strong>Audiolab, Classroom, Audimax</p> <p>_______________________________________________________________________________________</p> <p><strong>Contact:</strong><br> Tim Lübeck, Johannes M. Arend, and Christoph Pörschmann<br> TH Köln - University of Applied Sciences<br> Institute of Communications Engineering<br> Department of Acoustics and Audio Signal Processing<br> Betzdorfer Str. 2, D-50679 Cologne, Germany</p> <p><a href="https://www.th-koeln.de/personen/tim.luebeck/">https://www.th-koeln.de/personen/tim.luebeck/</a></p> <p><a href="https://www.th-koeln.de/personen/johannes.arend/">https://www.th-koeln.de/personen/johannes.arend/</a></p> <p><a href="http://www.th-koeln.de/personen/christoph.poerschmann/">https://www.th-koeln.de/personen/christoph.poerschmann/</a><br> <br> </p>
6 DoF Directional Room Impulse Response Dataset Measured over a Dense Loudspeaker Grid (6DRIR-DL)
<p>The 6DoF directional RIR dataset (aka <strong>6DRIR-DL</strong>) includes room impulse responses (RIRs) measured by <strong>nine</strong> spherical microphone arrays (SMAs; Zylia ZM-1S) distributed in a semi-cuboid room. <strong>6DRIR-DL</strong> is specialized by its massive loudspeaker positions (<strong>392</strong> locations), which were incorporated for the 6DOF source localization task. The inter-element spacing between loudspeakers is only 8 cm, so the dataset can be utilized to validate a sound source localization algorithm for closely positioned multiple sound sources.</p>
ASN Database - v3.2 - Database of Simulated Room Impulse Responses for Acoustic Sensor Networks Deployed in Complex Multi-Source Acoustic Environments
<p>We present a large set of simulated room impulse responses for a multi-room apartment. The simulated apartment models a real vacation apartment for which a recorded set of audio data has already been made available in the context of the DCASE challenges. The impulse responses were rendered using a dense grid of sources and receivers by means of a hybrid auralization algorithm based on a low-order image-source method and deterministic cone tracing. The proposed data set can be used to generate a wide variety of acoustic scenes which, in turn, can benefit numerous data-demanding machine-learning algorithms.<br> <br> To obtain more information on the database, please visit <a href="https://github.com/Jearde/asn-database">the website</a>.<br> <strong>Please read the license file (available in the GitHub repository) before using the database.</strong></p>
TAU Spatial Room Impulse Response Database (TAU-SRIR DB)
<p><strong>DESCRIPTION</strong></p> <p>The <strong>TAU Spatial Room Impulse Response Database (TAU-SRIR DB)</strong> database contains spatial room impulse responses (SRIRs) captured in various spaces of Tampere University (TAU), Finland, for a fixed receiver position and multiple source positions per room, along with separate recordings of spatial ambient noise captured at the same recording point. The dataset is intended for emulation of spatial multichannel recordings for evaluation and/or training of multichannel processing algorithms in realistic reverberant conditions and over multiple rooms. The major distinct properties of the database compared to other databases of room impulse responses are:</p> <ul> <li>Capturing in a high resolution multichannel format (32 channels) from which multiple more limited application-specific formats can be derived (e.g. tetrahedral array, circular array, first-order Ambisonics, higher-order Ambisonics, binaural).</li> <li>Extraction of densely spaced SRIRs along measurement trajectories, allowing emulation of moving source scenarios.</li> <li>Multiple source distances, azimuths, and elevations from the receiver per room, allowing emulation of complex configurations for multi-source methods.</li> <li>Multiple rooms, allowing evaluation of methods at various acoustic conditions, and training of methods with the aim of generalization on different rooms.</li> </ul> <p>The RIRs were collected by staff of TAU between 12/2017 - 06/2018, and between 11/2019 - 1/2020. The data collection received funding from the European Research Council, grant agreement 637422 <a href="https://cordis.europa.eu/project/id/637422">EVERYSOUND</a>.</p> <p><strong><em>NOTE</em></strong><em>: This database is a work-in-progress. We intend to publish additional rooms, additional formats, and potentially higher-fidelity versions of the captured responses in the near future, as new versions of the database in this repository.</em></p> <p> </p> <p><strong>REPORT AND REFERENCE</strong></p> <p>A compact description of the dataset, recording setup, recording procedure, and extraction can be found in:</p> <p>Politis., Archontis, Adavanne, Sharath, & Virtanen, Tuomas (2020). <strong>A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection</strong>. In <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)</em>, Tokyo, Japan.</p> <p>available <a href="https://dcase.community/documents/workshop2020/proceedings/DCASE2020Workshop_Politis_88.pdf">here</a>. A more detailed report specifically focusing on the dataset collection and properties will follow.</p> <p> </p> <p><strong>AIM</strong></p> <p>The dataset can be used for generating multichannel or monophonic mixtures for testing or training of methods under realistic reverberation conditions, related to e.g. multichannel speech enhancement, acoustic scene analysis, and machine listening, among others. It is especially suitable for the follow application scenarios:</p> <ul> <li>monophonic and multichannal reverberant single- or multi-source speech in multi-room reverberant conditions</li> <li>monophonic and multichannel polyphonic sound events in multi-room reverberant conditions </li> <li>single-source and multi-source localization in multi-room reverberant conditions, in static or dynamic scenarios</li> <li>single-source and multi-source tracking in multi-room reverberant conditions, in static or dynamic scenarios</li> <li>sound event localization and detection in multi-room reverberant conditions, in static or dynamic scenarios</li> </ul> <p> </p> <p><strong>SPECIFICATIONS</strong></p> <p>The SRIRs were captured using an [Eigenmike](https://mhacoustics.com/products) spherical microphone array. A [Genelec G Three loudspeaker](https://www.genelec.com/g-three) was used to playback a maximum length sequence (MLS) around the Eigenmike. The SRIRs were obtained in the STFT domain using a least-squares regression between the known measurement signal (MLS) and far-field recording independently at each frequency. In this version of the dataset the SRIRs and ambient noise are downsampled to 24kHz for compactness.</p> <p>The currently published SRIR set was recorded at nine different indoor locations inside the Tampere University campus at Hervanta, Finland. Additionally, 30 minutes of ambient noise recordings were collected at the same locations with the IR recording setup unchanged. SRIR directions and distances differ with the room. Possible azimuths span the whole range of $\phi\in[-180,180)$, while the elevations span approximately a range between $\theta\in[-45,45]$ degrees. The currently shared measured spaces are as follows:</p> <ol> <li>Large open space in underground bomb shelter, with plastic-coated floor and rock walls. Ventilation noise. Circular source trajectory.</li> <li>Large open gym space. Ambience of people using weights and gym equipment in adjacent rooms. Circular source trajectory.</li> <li>Small classroom (PB132) with group work tables and carpet flooring. Ventilation noise. Circular source trajectory.</li> <li>Meeting room (PC226) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory.</li> <li>Lecture hall (SA203) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory.</li> <li>Small classroom (SC203) with group work tables and carpet flooring. Ventilation noise. Linear source trajectory.</li> <li>Large classroom (SE203) with hard floor and rows of desks. Ventilation noise. Linear source trajectory.</li> <li>Lecture hall (TB103) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory.</li> <li>Meeting room (TC352) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory.</li> </ol> <p>The measurement trajectories were organised in groups, with each group being specified by a circular or linear trace at the floor at a certain distance from the z-axis of the microphone. For circular trajectories two ranges were measured, a <em>close</em> and a <em>far</em> one, except room TC352, where the same range was measured twice, but with different furniture configuration and open or closed doors. For linear trajectories also two ranges were measured, <em>close</em> and <em>far</em>, but with linear paths at either side of the array, resulting in 4 unique trajectory groups, with the exception of room SA203 where 3 ranges were measured resulting on 6 trajectory groups. Linear trajectory groups are always parallel to each other, in the same room.</p> <p>Each trajectory group had multiple measurement trajectories, following the same floor path, but with the source at different heights. </p> <p>The SRIRs are extracted from the noise recordings of the slowly moving source across those trajectories, at an angular spacing of approximately every 1 degree from the microphone. Instead of extracting SRIRs at equally spaced points along the path (e.g. every 20cm), this extraction scheme was found more practical for synthesis purposes, making emulation of moving sources at an approximately constant angular speed easier.</p> <p>More details on the trajectory geometries can be found in the <strong>README</strong> file and the <strong>measinfo.mat</strong> file.</p> <p> </p> <p><strong>RECORDING FORMATS</strong></p> <p>As with the DCASE2019-2021 datasets, currently the database is provided in two formats, first-order Ambisonics, and a tetrahedral microphone array - both derived from the Eigenmike 32-channel recordings. For more details on the format specifications, check the README. </p> <p>We intend to add additional formats of the database, of both higher resolution (e.g. higher-order Ambisonics), or lower resolution (e.g. binaural).</p> <p> </p> <p><strong>REFERENCE DOAs</strong></p> <p>For each extracted RIR across a measurement trajectory there is a direction-of-arrival (DOA) associated with it, which can be used as the reference direction for sound source spatialized using this RIR, for training or evaluation purposes. The DOAs were determined acoustically from the extracted RIRs, by windowing the direct sound part and applying a broadband version of the MUSIC localization algorithm on the windowed multichannel signal.</p> <p>The DOAs are provided as Cartesian components [x, y, z] of unit length vectors.</p> <p> </p> <p><strong>SCENE GENERATOR</strong></p> <p>A set of routines is shared, here termed <em>scene generator</em>, that can spatialize a bank of sound samples using the SRIRs and noise recordings of this library, to emulate scenes for the two target formats. The code is similar to the one used to generate the <a href="https://doi.org/10.5281/zenodo.5476980"><strong>TAU-NIGENS Spatial Sound Events 2021</strong></a> dataset, and has been ported to Python from the original version written in Matlab.</p> <p>The generator can be found [**<strong>here</strong>**](https://github.com/danielkrause/DCASE2022-data-generator), along with more details on its use. </p> <p>The generator at the moment is set to work with the <a href="https://zenodo.org/record/2535878">NIGENS</a> sound event sample database, and the <a href="https://zenodo.org/record/4060432">FSD50K</a> sound event database, but additional sample banks can be added with small modifications.</p> <p>The dataset together with the generator has been used by the authors in the following public challenges:</p> <p>- <a href="https://dcase.community/challenge2019/task-sound-event-localization-and-detection">DCASE 2019 Challenge Task 3</a>, to generate the <strong>TAU Spatial Sound Events 2019</strong> dataset (<a href="https://doi.org/10.5281/zenodo.2599196">development</a>/<a href="https://doi.org/10.5281/zenodo.3377088">evaluation</a>)</p> <p>- <a href="https://dcase.community/challenge2020/task-sound-event-localization-and-detection">DCASE 2020 Challenge Task 3</a>, to generate the <a href="https://doi.org/10.5281/zenodo.4064792"><strong>TAU-NIGENS Spatial Sound Events 2020</strong></a> dataset</p> <p>- <a href="https://dcase.community/challenge2021/task-sound-event-localization-and-detection">DCASE2021 Challenge Task 3</a>, to generate the <a href="https://doi.org/10.5281/zenodo.5476980"><strong>TAU-NIGENS Spatial Sound Events 2021</strong></a> dataset</p> <p>- <a href="https://dcase.community/challenge2022/task-sound-event-localization-and-detection">DCASE2022 Challenge Task 3</a>, to generate additional <a href="https://doi.org/10.5281/zenodo.6406873"><strong>SELD synthetic mixtures for training the task baseline</strong></a></p> <p><em><strong>NOTE</strong>: The current version of the generator is work-in-progress, with some code being quite "rough". If something does not work as intended or it is not clear what certain parts do, please contact us.</em></p> <p> </p> <p><strong>DATASET STRUCTURE</strong></p> <p>The dataset contains a folder of the SRIRs (<strong>TAU-SRIR_DB</strong>), with all the SRIRs per room in a single MAT file. The file <strong>rirdata.mat</strong> contains some general information such as sample rate, format specifications, and most importantly the DOAs of every extracted SRIR. The file <strong>measinfo.mat</strong> contains measurement and recording information in each room. Finally, the dataset contains a folder of spatial ambient noise recordings (<strong>TAU-SNoise_DB</strong>), with one subfolder per room having two audio recordings fo the spatial ambience, one for each format, FOA or MIC. For more information on how to SRIRs and DOAs are organized, check the README.</p> <p> </p> <p><strong>DOWNLOAD</strong></p> <p>The files <em>TAU-SRIR_DB.z01</em>, ..., <em>TAU-SRIR_DB.zip</em> contain the SRIRs and measurement info files.</p> <p>The files <em>TAU-SNoise_DB.z01</em>, ..., <em>TAU-SNoise_DB.zip</em> contain the ambient noise recordings.</p> <p>Download the zip files and use your preferred compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal:</p> <p>Combine the split archive to a single archive:</p> <pre><code>zip -s 0 split.zip --out single.zip</code></pre> <p>Extract the single archive using unzip:</p> <pre><code>unzip single.zip</code></pre> <p> </p> <p><strong>LICENSE</strong></p> <p>The database is published under a custom **<strong>open non-commercial with attribution</strong>** license. It can be found in the `LICENSE.txt` file that accompanies the data.</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>
Dataset of simulated room impulse responses in three coupled rooms
<p>This dataset accompanies the publication</p> <blockquote> <div> <div> <div> <p>Georg Götz, Teodors Kerimovs, Sebastian J. Schlecht, and Ville Pulkki. Dynamic late reverberation rendering using the common-slope model. In Proceedings of the AES 6th International Conference on Audio for Games, Tokyo, Japan, April 2024.</p> </div> </div> </div> </blockquote> <div> <div> <div> <p> </p> <div> <div> <div> <p>The dataset includes room acoustic simulations conducted with the hybrid simulation suite Treble, using a transition frequency of approximately 750 Hz between wave-based and GA simulation. We simulated the coupled room geometry depicted in the file room_geometry2.pdf. The orange × indicates the source position, and receivers were uniformly distributed on the xy-plane with 0.3 m resolution. Each room has a height of 3 m and exhibits a uniform absorption distribution. Room R2 is the most reverberant with an absorption coefficient similar to concrete (αR2 = 0.01), whereas R1 and R3 are significantly less reverberant with αR1 = 0.2 and αR3 = 0.1, respectively.</p> <p>The dataset also includes the common-slope analysis results for the omnidirectional responses and also for the sector-based analysis as described in the paper. Please also refer to the following paper for more details on the common-slope analysis:</p> <blockquote> <p>Georg Götz, Sebastian J. Schlecht, and Ville Pulkki. Common-slope modeling of late reverberation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 31, pp. 3945–3957, September 2023. doi: <a href="https://doi.org/10.1109/TASLP.2023.3317572" target="_blank" rel="noopener">10.1109/TASLP.2023.3317572</a></p> </blockquote> </div> </div> </div> </div> </div> </div>
Room Impulse Responses (RIRs) for B-format recordings
<p>Room Impulse Responses (RIRs) for B-format recordings.</p> <p>This dataset in .mat format has been utilized for generating the reverberant speech mixtures for my recent work named "Exploiting Angular and Spectral Features for B-Format Speech Separation with Deep Neural Networks".</p>
BRUDEX Database: Binaural Room Impulse Responses with Uniformly Distributed External Microphones
<p>There is an emerging need for comparable data for multi-microphone processing, particularly in acoustic sensor networks. However, commonly available databases are often limited in the spatial diversity of the microphones or only allow for particular signal processing tasks. In this paper, we present a database of acoustic impulse responses and recordings for a binaural hearing aid setup, 36 spatially distributed microphones spanning a uniform grid of (5x5) m^2 and 12 source positions. This database can be used for a variety of signal processing tasks, such as (multi-microphone) noise reduction, source localization, and dereverberation, as the measurements were performed using the same setup for three different reverberation conditions (T_60≈{310, 510, 1300} ms). The usability of the database is demonstrated for a noise reduction task using a minimum variance distortionless response beamformer based on relative transfer functions, exploiting the availability of spatially distributed microphones.</p> <p><br>An example how to load a impulse responses corresponding to the 'low' reverberation condition for the speaker located at 60 deg using MATLAB:<br> dataStruct = loadRIR('low',60,1,<basePATH>);%<basePATH>: path where database is located on local machine<br>For further MATLAB examples, please consider "wrapper_loadDataFromDB.m" in the "matlabScripts.zip" archive file.</p> <p>An example how to load a impulse responses corresponding to the 'low' reverberation condition for the speaker located at 60 deg using Python:<br> dataloader = wrapper.BRUDEXDataloader()# <basePATH> is implicitly set to that path, where the file "wrapper.py" is located on local machine<br> dataStruct = dataloader.load_rir(reverberation_condition='low', direction_of_arrival = 60, ha_av = 1,e_mic_run = None)<br>For further Python examples, please consider "main.py" in the "pythonScripts.zip" archive file.</p> <p>Caution: We noticed some problems with the download of the databse when using the command line (e.g., via the zenodo_get, wget, or curl commands). These problems don't seem to appear when downloading the files with the "Download" buttons on the website instead.</p> <p>Caution 2: When processing microphone signals, which are recorded with microphones that are placed *behind* loudspeakers, one can expect direct-path problems.</p> <p>Caution 3: For the recordings of the noise signals, four loudspeakers were placed at about 170 cm from (and facing) the corners of the room. That is why the noise is approximately spatially diffuse only in the vicinity of the center of the room and rather spatially coherent in the corners of the room.</p> <p>Caution 4: Oppposed to Zenodos information, the database does not contain 3 TB of data but about 200 GB.</p> <p> </p> <p>Reference:</p> <p>D. Fejgin, W. Middelberg, and S. Doclo,<br>“BRUDEX database: Binaural room impulse responses with uniformly distributed external microphones,”<br>in Proc. ITG Conference on Speech Communication, Aachen, Germany, Sep. 2023, pp. 1–5.</p> <p>@InProceedings{Fejgin2023,<br> author = {D. {Fejgin} and W. {Middelberg} and S. {Doclo}},<br> booktitle = {Proc. ITG Conference on Speech Communication},<br> title = {{BRUDEX} Database: Binaural Room Impulse Responses with Uniformly Distributed External Microphones},<br> pages = {1-5},<br> month = {Sep.},<br> year = {2023},<br> address = {Aachen, Germany}<br>}</p>
Binaural room impulse responses for interactive listener translation in a seminar room
<p>With the goal to study the perception of position dynamic binaural synthesis for walking listeners wearing headphones, a set of binaural room impulse responses (BRIRs) and omnidirectional RIRs was measured. The measurements were conducted with a Kemar 45BA dummy head in a seminar room (9.9m x 4.7m x 3.1, T60 = 1.0s). Two loudspeakers Genelec 1030A served as sound sources. The BRIRs were measured at 16 positions arranged along a line towards the frontal loudspeaker with distances ranging from 1m - 8.50m and a positional resolution of 50cm. The second source was placed at the side of the line. The BRIR were determined for a full 360° rotation in 4° steps for each of the measurement positions. In addition, omnidirectional RIRs were measured at the 16 listening positions.</p> <p>As a second test case, on a different day both loudspeakers were turned by 180° to be pointing away from the translation line. BRIRs were captured for the positions with the same angular resolution. The interior slightly differed from the first measurement. The changes are documented with photos of the room and the measurement setup.</p>
Directional Room Impulse Response Measurement - PhD Thesis Data
<p>Room acoustic simulation result database for the PhD thesis "Directional Room Impulse Response Measurement"</p>
DRR-scaled Individual Binaural Room Impulse Responses
<p>Dataset of recorded individual binaural room impulse responses (BRIRs). The BRIRs are scaled their Direct-to-Reverberant-Energy-Ratio (DRR). The DRR is changed by an amplification or damping of the reverberant part relative to the direct sound of a measured BRIR. The change of the BRIR is conducted 3 ms after the direct sound, avoiding effects on the head-related part. The DRRs are calculated for 70 steps with 46 damping steps at normalized amplitude from zero to one, 23 amplification steps at normalized amplitude from one to 1.3, and the original DRR.</p>
HOMULA-RIR: A Room Impulse Response Dataset for Teleconferencing and Spatial Audio Applications Acquired Through Higher-Order Microphones and Uniform Linear Microphone Arrays
<p>In this paper, we present HOMULA-RIR, a dataset of room impulse responses (RIRs) acquired using both higher-order microphones (HOMs) and a uniform linear array (ULA), in order to model a remote attendance teleconferencing scenario. Specifically, measurements were performed in a seminar room, where a 64-microphone ULA was used as a multichannel audio acquisition system in the proximity of the speakers, while HOMs were used to model 25 attendees actually present in the seminar room. The HOMs cover a wide area of the room, making the dataset suitable also for applications of virtual acoustics. Through the measurement of the reverberation time and clarity index, and sample applications such as source localization and separation we demonstrate the effectiveness of the HOMULA-RIR dataset.</p>
CT-AudioLink : Room Impulse Responses - http://ct-audiolink.gr
<p>Room Impulse responses measured during the project CT - Audiolink. The responses correspond to various measuring positions at cultural heritage buildings of Thrace region. These are:</p> <p>AKA = Kuyumjoglou Museum<br> EP = Church of Panagia<br> GJ = Yenni Mosque<br> IM = Imaret of Komotini ( Room A = IMA , Room B = IMB )<br> </p>
Dataset of measured binaural room impulse responses for use in an position-dynamic auditory augmented reality application
<p>The dataset includes measured binaural room impluse responses (BRIRs) using a head and torso simulator (KEMAR). The measurements are realized for six loudspeaker positions. Five loudspeakers are placed in a standardized five-channel surround setup with the left and right speaker at +/-30° from the center speaker. The left-surround and right-surround speaker are placed at +/-120°. The distance to the midpoint of this setup is 3.5 m. The sixth loudspeaker is placed 1.35 m from the midpoint and 150° to the right side of the setup. Loudspeakers of type Geithain R906 are used.</p> <p>The BRIRs are measured at nine positions within the loudspeaker setup. For each position the artificial head is turned 360° in the horizontal plane with a step size of 5°. Next to the midpoint position of the setup, frontal, backward, and lateral positions are measured. The covered plane size is 4m x 4m. The figure 'scheme.png' gives an overview about the names and setup (included in the download of the dataset).</p> <p>A TV studio at the Technische Universität Ilmenau is used as room for the measurements. The figure 'studio.jpg' (included in the download of the dataset) shows the room in a prior configuration as a TV studio (the left wall as scenery setting is removed). The setup is build up in the left side of the room (gray floor area). The room has a total size of 19.5m x 11.5m x 5.5m. The reverberation time (RT60 from 80 Hz to 18 kHz) is approx. 0.7 s. The C50 and C80 are 15dB and 17dB.</p> <p>The BRIR dataset is used for the synthesis of new BRIRs at diffferent positions in the room using methods described and evaluated in [1] and [2]. Please feel free to use the measured BRIRs for your research and your project! </p> <p>---<br> [1] Brandenburg, K., Cano, E., Klein, F., Köllmer, T., Lukashevich, H., Neidhardt, A., Sloma, U., and Werner, S., “Plausible Augmentation of Auditory Scenes Using Dynamic Binaural Synthesis for Personalized Auditory Realities”, to be published in Proc. of: Conference of the Audio Engineering Society (AES) Audio for Virtual and Augmented Reality, USA, 2018.</p> <p><br> [2] Werner, S., Neidhardt, A., Klein, F., and Brandenburg, K., “Comparison of Different Methods to Create an Interactive Augmented Auditory Reality Scenario Using Sparse Binaural Room Impulse Response Measurements”, in Proc. of DAGA 2018, Garching, Germany, 2018.</p> <p> </p>
360° Binaural Room Impulse Response (BRIR) Database for 6DOF spatial perception research
<p>by Applied Psychoacoustics Lab, University of Huddersfield</p> <p> </p> <p>Authors: Bogdan Bacila and Hyunkook Lee</p> <p>bogdan.bacila@hud.ac.uk, h.lee@hud.ac.uk</p> <p> </p> <p><strong>Description</strong></p> <p>An open-access database for 360° binaural room impulse responses (BRIR) captured in a reverberant concert hall. Head-rotated BRIRs were acquired with 3.6° angular resolution for each of 13 different receiver positions, using a custom-made head-rotation system that was automated and integrated with the Huddersfield Acoustical Analysis Research Toolbox. The BRIRs are provided in the SOFA format. The library also contains impulse responses captured using a first-order Ambisonic microphone and an omnidirectional microphone. It is expected that the database would be useful for studying the perception of spatial attributes in a six degrees-of-freedom context.</p> <p> </p> <p><strong>Folder Structure</strong></p> <p>The impulse responses are organised into two main folders:</p> <p>* Binaural: Contains the SOFA files and MATLAB files for each position, with a 3.6° angular resolution, recorded with the Neumann KU100 binaural head.</p> <p>* FOA: Contains the First Order Ambisonics audio files in A format and B format for each individual position, recorded with an Sennheiser Ambeo microphone in an end-fire configuration. </p> <p> </p> <p><strong>Naming Convention</strong></p> <p>The files are named after their relative position on the stage and the distance from the stage:</p> <p>* C = Centre</p> <p>* L = Left</p> <p>* LW = Left Wide</p> <p> </p> <p><strong>License</strong></p> <p>This project is licensed under the CC-BY-4.0 License - see the License.txt file for details</p> <p> </p> <p><strong>Publication</strong></p> <p>This database was presented at the Audio Engineering Society 146th International Convention.</p> <p>Download link: http://www.aes.org/e-lib/browse.cfm?elib=20371</p> <p> </p> <p><strong>Referencing</strong></p> <p>If you use the database for your research, please reference it as follows.</p> <p>Bacila, B. I., & Lee, H. (2019). 360° Binaural Room Impulse Response (BRIR) Database for 6DOF Spatial Perception Research. Presented at the Audio Engineering Society Convention 146, Dublin, e-Brief 513</p>
Simulated Room Impulse Response for 44.1k Audio
<p>We randomly simulated a collection of Room Impulse Response filters to simulate the 44.1kHz speech room reverberation using an open-source tool (<a href="https://github.com/sunits/rir">https://github.com/sunits/rir</a>_simulator_python). The meters of height, width, and length of the room are sampled randomly in a uniform distribution U(1,12). The placement of the microphone is then randomly selected within the room space. For the placement of the sound source, we first determined the distance between the microphone and sound source, which is randomly sampled in a Gaussian distribution N(\mu,\sigma^2), \mu=2, \sigma=4. If the sampled value is negative or greater than five meters, we will sample the distance again until it meets the requirement. After determined the distance between the microphone and sound source, the placement of the sound source is randomly selected on the sphere centered at the microphone. The RT60 value we choose comes from the uniform distribution U(0.05,1.0). For the pickup pattern of the microphone, we randomly choose from types omnidirectional and cardioid. </p> <p>If you found this dataset helpful, please consider citing: </p> <blockquote> <pre>@article{liu2021voicefixer, title={VoiceFixer: Toward General Speech Restoration with Neural Vocoder}, author={Liu, Haohe and Kong, Qiuqiang and Tian, Qiao and Zhao, Yan and Wang, DeLiang and Huang, Chuanzeng and Wang, Yuxuan}, journal={arXiv preprint arXiv:2109.13731}, year={2021} }</pre> </blockquote>
A dataset of measured spatial room impulse responses for the transition between coupled rooms
<p>For a detailed description of the measurement and analysis methods, please see:</p> <p>McKenzie, T., Schlecht, S. J., and Pulkki, V. (2021). Acoustic Analysis and Dataset of Transitions between Coupled Rooms. <em>IEEE International Conference on Acoustics, Speech and Signal Processing.</em></p> <p> </p> <p>This dataset contains measured spatial room impulse responses for the transition between coupled rooms. Four coupled room pairs are included:</p> <ul> <li>Meeting Room to Hallway</li> <li>Office to Anechoic Chamber</li> <li>Office to Kitchen</li> <li>Office to Stairwell</li> </ul> <p>All were recorded at the Aalto University campus using a Genelec 8331A coaxial loudspeaker and an mh acoustics Eigenmike (32 capsule spherical microphone array for fourth order spherical harmonic capture). Each transition features 101 measurements in 5cm intervals from 2.5m inside the first room to 2.5m inside the second room. Transitions are repeated four times corresponding to four different source positions: two inside each room, one which has no continuous line-of-sight with the microphone when in the opposing room, and one which retains a direct continuous line-of-sight with the microphone for all measurement positions. </p> <p>The spatial room impulse responses are downloadable in either Spatially Oriented Format for Acoustics (SOFA) and Wav formats. Supplementary data includes amplitude plots, direct-to-reverberant graphs, estimated direction-of-arrival plots and scaled illustrations of room geometries and source positions. </p> <p>Changelog: </p> <p>V 1.0 - Initial version<br> V 1.1 - Added plots only download option<br> V 1.2 - Improved time alignment of impulse responses and changed normalisation to a single value relative to the maximum of the entire dataset<br> V 1.3 - SOFA files updated to latest Matlab API (1.1.3), 'SingleRoomDRIR' convention, with SourcePosition data corrected. The SOFA files for each transition are also now downloadable separately, in case the entire dataset is not required. <br> V 1.4 - SRIRs have been denoised using the technique described in https://www.aes.org/e-lib/browse.cfm?elib=21800 and available at https://github.com/chris-hld/Directional-Multi-Slope-Room-Impulse-Response-Denoising. This is particularly noticeable for measurements with a low SNR, such as where there is a large distance between source and receiver and occluded direct path. Note that the wav files have not been included in this release - see previous releases if wav files (not denoised) are required. SOFA files are renamed and available to download separately.</p>
Dataset of impulse responses from variable acoustics room Arni at Aalto Acoustic Labs
<p>A dataset of impulse responses collected in the variable acoustics laboratory Arni at Acoustics Lab of Aalto University, Espoo, Finland.</p> <p>IRs of 5342 configurations of sound absorption in Arni are included in the dataset. Each of them were measured using an omnidirectional sound source and 5 sound receivers. For each configuration, 5 IRs were captured. The total number of measurements in the dataset is 132 037.</p> <p>For more information and the reverberation time values estimated from the IRs, please see the paper "Calibrating the Sabine and Eyring formulas" by Karolina Prawda, Sebastian J. Schlecht, and Vesa Välimäki, in The Journal of the Acoustical Society of America</p> <p>The work leading to producing the dataset and related research was supported by the Nordic Sound and Music Computing Network---NordicSMC, NordForsk project number 86892.</p> <p> </p> <p><strong>How to read the files in the dataset:</strong></p> <p>The general style of file naming is:<br> IR_numClosed_NumberOfReflectivePanels_numComb_CombinationNumber_mic_ReceiverNumber_sweep_SweepNumber<br> for example, for 1 reflective panel, combination 87, IR obtained from 3rd out of 5 sweeps recorded by microphone number 2, the filename is:<br> IR_numClosed_1_numComb_87_mic_2_sweep_3.wav</p> <p>The dataset was divided into several files due to its total size. The files included in each .zip file are indicated in its name.</p> <p>If some of the sound files are missing, e.g. for a certain combination and microphone there are 4 IR recordings instead of 5, it means that the sweep was discarded in the pre-processing phase due to non-stationary noise.<br> Read more: Karolina Prawda, Sebastian J. Schlecht, and Vesa Välimäki, "Robust selection of clean swept-sine measurements in non-stationary noise", The Journal of the Acoustical Society of America 151, 2117-2126 (2022), doi: https://doi.org/10.1121/10.0009915</p> <p>The specific combinations with indication whether the panel was in a reflective (represented by figure 0) or an absorptive (represented by figure 1) state are listed in <strong>combinations_setup.csv</strong> file.<br> The first column contains the combination number (0--5341), the first row is the panel number (1--55).</p> <p><strong>Arni_panels_numbers.pdf</strong> contains a diagram showing the panel numbers and locations.</p> <p><strong>Arni_layout.jpg</strong> contains a drawing specifying the positions of source source, sound receivers, and other elements of the interior.</p> <p> </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.