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306 results for “impulsivity”
PAN-AR: A Multimodal Dataset of Higher-Order Ambisonics Room Impulse Responses, Ambient Noise and Spherical Pictures
<h1>PAN-AR</h1> <p>This is <strong>PAN-AR</strong> (Panoramas, Ambient Noise & Ambisonics RIRs), a dataset described in the following <a href="https://doi.org/10.1145/3678299.3678332" target="_blank" rel="noopener">paper</a>:</p> <blockquote> <p>Filippo Denti, Davide Fantini, Federico Avanzini and Giorgio Presti. PAN-AR: A Multimodal Dataset of Higher-Order Ambisonics Room Impulse Responses, Ambient Noise and Spherical Pictures. In <em>Proceedings of the 19th International Audio Mostly Conference</em>, Milan, Italy, September 2024.</p> </blockquote> <p>The dataset includes Spatial Room Impulse Responses (SRIRs) in second-order Ambisonics format, ambient noise recordings, and spherical photos. These data have been captured in four environments with different configurations of the source and listener positions:</p> <ol> <li>Printer room</li> <li>Meeting room</li> <li>Classroom</li> <li>Underground parking area</li> </ol> <p>Panoramas and planimetries are provided in a temporary version. The final version with post-processed panoramas and complete planimetries will be available soon. An example of the final panoramas is provided for position A of the printer room, while an example of complete planimetry is provided for the printer and the meeting rooms.</p> <h2>SOFA</h2> <p>The SRIRs are also provided in SOFA format <a href="https://sofacoustics.org/data/database/pan-ar/" target="_blank" rel="noopener">here</a>.</p> <h2>How to cite</h2> <p>If you use the PAN-AR dataset, please cite the following <a href="https://doi.org/10.1145/3678299.3678332" target="_blank" rel="noopener">paper</a>:</p> <pre><code>@inproceedings{denti2024panar,</code><br><code> title = {{PAN-AR}: A Multimodal Dataset of Higher-Order Ambisonics Room Impulse Responses, Ambient Noise and Spherical Pictures},</code><br><code> author = {Denti, Filippo and Fantini, Davide and Avanzini, Federico and Presti, Giorgio},</code><br><code> year = {2024},</code><br><code> month = {September},</code><br><code> booktitle = {Proceedings of the 19th International Audio Mostly Conference (AM '24)},</code><br><code> location = {Milan, Italy},</code><br><code> publisher = {ACM},</code><br><code> isbn = {979-8-4007-0968-5/24/09},</code><br><code> doi = {10.1145/3678299.3678332}</code><br><code>}</code></pre>
Open Database of Spatial Room Impulse Responses at Detmold University of Music
<p>This repository contains an open source database of Spatial Room Impulse Responses (SRIR) captured at three different performance spaces of the Detmold University of Music. It includes the following rooms: </p> <ul> <li>Detmold Konzerthaus (medium sized concert hall, ~600 seats).</li> <li>Brahmssaal (small music chamber room, ~100 seats).</li> <li>Detmold Sommertheater (theater, ~300 seats).</li> </ul> <p>The collection contains approximately 600 multichannel RIRs corresponding to several source and receiver configurations. For each room we include measurement positions on stage and at the audience area captured with both an artificial head and an open microphone array compatible with the Spatial Decomposition Method (SDM).</p> <p>The Detmold Konzerthaus holds a large scale Wave Field Synthesis system and a Room Acoustic Enhancement System. SRIRs of an ensemble of focused sources on stage and with conditions of increased artificial reverberation are also included.</p> <p>If you use this dataset for your research, please cite our work:</p> <p>Amengual Gari, S. V.; Sahin, B.; Eddy, D; Kob, M.: <strong>"Open Database of Spatial Room Impulse Responses at Detmold University of Music"</strong>, <em>149th Convention of the Audio Engineering Society, </em>2020.</p> <p> </p> <p>The database is organized in 3 sets:</p> <p><strong>- Set A: </strong></p> <p>Source: Single Source measurements.</p> <p>Receiver: Open Array and Dummy Head.</p> <p>Rooms: BS, DST, KH</p> <p>Special configurations: Artificial reverberation, music stand on stage</p> <p><strong>- Set B: </strong></p> <p>Source: Loudspeaker and WFS orchestra</p> <p>Receiver: Open Array.</p> <p>Rooms: KH</p> <p><strong>- Set C:</strong></p> <p>Source: Loudspeaker orchestra</p> <p>Receiver: Dummy Head and Omni8 array</p> <p>Rooms: KH</p> <p> </p> <p>Further details on the measurement procedure and acoustical analysis of the RIRs can be found in the following publications:</p> <p><strong>Set A</strong></p> <p>Amengual Gari, S. V., Investigations on the Influence of Acoustics on Live Music Performance using Virtual Acoustic Methods, Ph.D. thesis, 2017.</p> <p>Amengual Garí, S. V.; Kob, M: "Investigating the impact of a music stand on stage using spatial impulse responses". 142nd Convention of the Audio Engineering Society, Berlin, May 2017.</p> <p><strong>Set B</strong></p> <p>Amengual Garí, S. V.; Pätynen, J.; Lokki, T.: "Physical and perceptual comparison of real and focused sound sources in a concert hall". Journal of the Audio Engineering Society, vol. 64 (12), pp. 1014-1025, December 2016.</p> <p><strong>Set C</strong></p> <p>Sahin, B., ““Investigation of the Detmold Concert Hall auditorium acoustics by comparing preference ratings and objective measurements.”, M.Sc. Thesis, 2017.</p> <p>Sahin, B., Amengual, S. V., and Kob, M., “Investigating listeners’ preferences in Detmold Concert Hall by comparing sensory evaluation and objective measurements,” Proc. 43th DAGA, Kiel, 2017.<br> </p>
Hydraulic scale model experiments on the two-dimensional run-up of impulse wave trains on steep to vertical slopes
<p>This dataset includes the experimental data and videos, which were generated during the study on the run-up of impulse wave trains at the Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich.</p>
A Free Database of Head-Related Impulse Response Measurements in the Horizontal Plane with Multiple Distances (MAT-Version)
<p>Head related impulse response measurements with the KEMAR dummy head performed in an anechoic chamber with a resolution of 1°. The impulse responses are provided for different distances and are accompanied by headphone compensation filters.</p> <p>This entry stores the measurements in the MAT format for use in Matlab/Octave. The measurements are identical to the once stored in the SOFA format available at <a href="https://doi.org/10.5281/zenodo.55418">https://doi.org/10.5281/zenodo.55418</a></p>
Binaural room impulse responses of an apartment-like environment
<p>Measured Binaural Room Impulse Responses (BRIR) of the ADREAM Laboratory, LAAS-CNRS, Toulouse, France. The measurements are described in detail in this publication:</p> <p>F. Winter, H. Wierstorf, A. Podlubne, T. Forgue, J. Manhès, M. Herrb, S. Spors, A. Raake, and P. Danès, "Database of binaural room impulse responses of an apartment-like environment," Proc. of 140th Aud. Eng. Soc. Conv., Paris, 2016</p> <p>Abstract of the Publication:</p> <p>We present a database of measured binaural room impulse responses (BRIRs) captured in an apartment-like environment. The BRIRs were measured for four different sound source positions, each combined with four listener positions with a head-orientation varying in the range of +-78° with 2° resolution. Additionally, BRIRs for 20 listener positions along a trajectory connecting two of the four positions were measured, each with a fixed head-orientation. The data is provided in the Spatially Oriented Format for Acoustics (SOFA) and it is freely available under Creative Commons (CC-BY-4.0). It can be used to simulate complex acoustic scenes in order to study the process of auditory scene analysis for humans and machines.</p>
Head-related impulse responses of a loudspeaker array
<p>Head-related impulse responses measured with a KEMAR dummy head of a loudspeaker array consisting of 13 loudspeakers. The dummy head was placed at three different positions, which allows to combine the measurements to an loudspeaker array consisting of 35 loudspeakers.</p> <p>The measurement equipment was exactly the same as in http://dx.doi.org/10.5281/zenodo.55418</p>
A Free Database of Head-Related Impulse Response Measurements in the Horizontal Plane with Multiple Distances
<p>Head related impulse response measurements with the KEMAR dummy head performed in an anechoic chamber with a resolution of 1°. The impulse responses are provided for different distances and are accompanied by headphone compensation filters.</p> <p>For details have a look at README.md.</p> <p>The same measurement can be downloaded as MAT files at <a href="https://doi.org/10.5281/zenodo.4459911">https://doi.org/10.5281/zenodo.4459911</a></p> <p>This dataset is further described in (see the PDF file)</p> <p>H. Wierstorf, M. Geier, A. Raake, S. Spors - A Free Database of Head-Related<br> Impulse Response Measurements in the Horizontal Plane with Multiple Distances.<br> In 130th AES Conv. 2011, eBrief 6.</p> <p> </p>
Binaural room impulse responses recorded with KEMAR of a 19-channel linear loudspeaker array
<p>BRIRs for 19 different loudspeakers placed in room Calypso at the Telefunken-building of TU Berlin were measured. The room is a studio listening room. The 19 loudspeakers constituted a linear loudspeaker array with a inter-loudspeaker distance of roughly 15cm. The measurement was done with the KEMAR (type 45BA) with the corresponding large ears (type KB0065 and KB0066) and Fostex PM0.4 loudspeakers. The dummy head was rotated from −90° to 90° in 1° steps. The measurement was repeated with the head wearing AKG K601 open headphones.</p>
Binaural room impulse responses recorded with KEMAR in a mid-size lecture hall
<p>The binaural room impulse responses (BRIRs) were measured at the mid-size lecture room Auditorium 3 at the<br> Telefunken-building of TU Berlin. They were measured for six different loudspeaker positions. The head of the dummy head was rotated with a resolution of 1° ranging from -90° to 90°. The measurement equipment was the same as described in Wierstorf et al. [1]</p> <p> </p> <p>[1] Wierstorf, H., Geier, M., Raake, A., Spors, S. (2011) “A Free Database of Head-Related Impulse Response Measurements in the Horizontal Plane with Multiple Distances,” 130th AES Convention, eBrief 6</p>
Spatial impulse wave
<p>ENGLISH<br> <br> Landslides and avalanches in natural lakes and reservoirs may generate so-called impulse waves. The run-up effects of these waves at the shore are similar to those of tsunamis. Hydraulic experiments in the laboratory help to estimate key wave characteristics, including the wave height and celerity. The picture presents two images of an experiment in the wave basin of the Laboratory of Hydraulics, Hydrology and Glaciology (VAW) at ETH Zurich. At the upper left, the moment shortly after the slide has hit the water surface is shown. The slide transfers its kinetic energy to the water and generates a wave. In the section on the lower right, the waves have propagated circularly from the impact location. The water is dyed white so that a grid can be projected onto the surface. During the experiment, this raster projection is simultaneously filmed by several cameras and the analysis of the image data allows for an accurate determination of the wave height decay.<br> <br> GERMAN<br> <br> Erdrutsche und Lawinen in natürliche Seen oder Stauseen können sogenannte Impulswellen auslösen. Die Auswirkungen beim Auflaufen dieser Wellen am Ufer sind mit denen von Tsunamis vergleichbar. Hydraulische Experimente im Labor helfen dabei, massgebliche Welleneigenschaften, wie beispielsweise die Höhe oder die Ausbreitungsgeschwindigkeit, abzuschätzen. Das Bild stellt zwei Aufnahmen eines Experiments im Wellenbecken der Versuchsanstalt für Wasserbau, Hydrologie und Glaziologie der ETH Zürich zu unterschiedlichen Zeitpunkten dar. Oben links ist der Moment kurz nach dem Auftreffen des Rutsches auf die Wasseroberfläche zu sehen. Der Rutsch überträgt dabei seine Bewegungsenergie auf das Wasser und erzeugt eine Welle. Im Ausschnitt unten rechts haben sich die Wellen kreisförmig von der Eintauchstelle weg ausgebreitet. Das Wasser ist weiss eingefärbt, damit ein Raster auf die Oberfläche projiziert werden kann. Diese Rasterprojektion wird während des Experiments gleichzeitig von mehreren Kameras gefilmt und die Auswertung der Bilddaten ermöglicht eine genaue Bestimmung der Wellenhöhenabnahme.</p>
Room Impulse Response Dataset: Niels Bohr Institute - Auditorium A
<h1>Dataset description</h1> <p>This dataset is comprised of acoustic Room Impulse Response (RIR) measurements using different measurement equipment at the iconic Auditorium A, in the Niels Bohr Institute, Copenhagen. The measurements were carried out during the days July 22<sup>nd</sup> & 23<sup>rd</sup>, 2023.</p> <p>The dataset includes:</p> <ul> <li><strong><em>rir_NBI_line.h5</em></strong> – Sequential measurements over a line using a robotic arm (UR5, Universal Robots) for microphone positioning.</li> <li><strong><em>rir_NBI_em32.h5</em></strong> – Distributed RIRs measured with the 32-channel spherical microphone array Eigenmike (em32, mh Acoustics).</li> <li><strong><em>rir_NBI_em32.sofa – </em></strong>SOFA format of the previous file.</li> </ul> <h2>The room</h2> <p>The Auditorium has a volume of approximately 126 m3 and is roughly rectangular with a tilted floor for the audience. The room was fully furnished with classroom equipment when measured. On one side wall, there are several interspaced windows.</p> <p>The reverberation time in octave bands is:</p> <table> <tbody> <tr> <td> <p>F (Hz)</p> </td> <td> <p>32</p> </td> <td> <p>63</p> </td> <td> <p>126</p> </td> <td> <p>250</p> </td> <td> <p>500</p> </td> <td> <p>1k</p> </td> <td> <p>2k</p> </td> <td> <p>4k</p> </td> <td> <p>8k</p> </td> <td> <p>16k</p> </td> </tr> <tr> <td> <p>T30 (s)</p> </td> <td> <p>0.75</p> </td> <td> <p>0.74</p> </td> <td> <p>0.61</p> </td> <td> <p>0.72</p> </td> <td> <p>0.80</p> </td> <td> <p>0.88</p> </td> <td> <p>1.03</p> </td> <td> <p>1.05</p> </td> <td> <p>0.85</p> </td> <td> <p>0.58</p> </td> </tr> </tbody> </table> <h2>The measurements</h2> <p>All measurements were obtained using an exponential sweep covering the frequencies 20-20k Hz. The specific duration of the sweep varies with the measurement setup and can be found in the corresponding file. The sampling rate is 48 kHz.</p> <p>The source is a two-way loudspeaker (BM6, Dynaudio), and its position was kept constant throughout the entire measurement campaign. The remaining equipment utilised was an audio interface (Fireface UCX, RME), a loudspeaker amplifier, a microphone amplifier (Nexus, B&K).</p> <p>The atmospheric conditions for each set of measurements are included in the corresponding file.</p> <h1>Related publications</h1> <p>This dataset is linked to the publication:</p> <ul> <li>Figueroa-Duran A. & Fernandez-Grande E., <em>Reconstruction of reverberant sound fields over large spatial domains. </em>In <em>J. Acoust. Soc. Am. (2025), </em><a href="https://doi.org/10.1121/10.0034833" target="_blank" rel="noopener">https://doi.org/10.1121/10.0034833</a></li> </ul> <h1>Contact</h1> <p>For any questions, please address them to <strong><em>anfig@dtu.dk</em></strong>.</p>
Dataset of Spatial Room Impulse Responses in a Variable Acoustics Room for Six Degrees-of-Freedom Rendering and Analysis
<p>Room acoustics measurements are used in many areas of audio research, from physical acoustics modelling and speech enhancement to virtual reality applications. This paper documents the technical specifications and choices made in the measurement of a dataset of spatial room impulse responses (SRIRs) in a variable acoustics room. Two spherical microphone arrays are used: the mh Acoustics Eigenmike em32 and the Zylia ZM-1, capable of up to fourth- and third-order Ambisonic capture, respectively. The dataset consists of three source and seven receiver positions, repeated with five configurations of the room's acoustics with varying levels of reverberation. Possible applications of the dataset include six degrees-of-freedom (6DoF) analysis and rendering, SRIR interpolation methods, and spatial dereverberation techniques. </p> <p>Accompanying paper on details of the dataset measurement: https://arxiv.org/abs/2111.11882</p> <p>Changelog:</p> <p>V 1.0 - Initial version.<br> V 1.1 - SOFA files updated to latest Matlab API (1.1.3), 'SingleRoomDRIR' convention, with SourcePosition and ListenerPosition z data corrected. Changed ListenerPosition and SourcePosition x data so that it follows the convention of origin in bottom left corner (rather than the previous bottom right). Fixed the swapped x and y labels in 6dof_source_and_receiver_positions.pdf.</p>
Dataset for: Application of Machine Learning for the Spatial Analysis of Binaural Room Impulse Responses
<p>This repository contains supplementary material for the paper titled `Application of Machine Learning for the Spatial<br> Analysis of Binaural Room Impulse Responses' Available at: <a href="http://dx.doi.org/10.3390/app8010105">dx.doi.org/10.3390/app8010105</a> . These programs and audio files are distributed in the hopes that they will prove useful under the Creative Commons Attribution 4.0, with no warranty; or the implied warranty of merchantability or fitness for a particular problem. Please give appropriate credit for use of the material provided in this repository back to the author. </p> <p>In order to use the MatLab code the Auditory Toolbox by Malcolm Slaney [1] and the Cochleagram function distributed by Bin Gao [2] are required.</p> <p>The python scrips require the following Python libraries to be installed: Numpy[3], SciPy[4] and Tensorflow [5].</p> <p>The MatLab code was tested using MatLab R2017a on a Computer running windows 7.</p> <p>The python code was tested using Python 3.2.5, using an anaconda Python environment - in windows command line.</p> <p>--</p> <p>The repository contains:</p> <p>Folders:</p> <p><br> 1.) neg90 - This folder contains the gaussian normalisation parameters stored as text files and the weights and biases for the trained neural network - these are all for the -90° rotation neural network.</p> <p>2.) pos90 - This folder contains the gaussian normalisation parameters stored as text files and the weights and biases for the trained neural network - these are all for the +90° rotation neural network.</p> <p>3.) testData - this folder contains pre-generated test data for the different binaural dummy head microphones, speaker, and signal type combinations.</p> <p>Python Scripts:</p> <p><br> 1.) AnalyseDoA.py - A python script that can be run to test the neural network using the pre-generated test data - running the script will allow the user to input the binaural dummy head, speaker, and signal type. The important variables generated by this script are DoA - the direction of arrival for each signal in the feature vector, and yDiff - the difference between the predicted DoA and the expected direction of arrival</p> <p>2.) DirectionAnalysis.py - This python file contains a set of function that are used to define the neural network, and run it. The function called DoAPrediction takes the feature vector generated by the MatLab code as its input argument, these features will then be passed to the neural network, and the output of this function is the direction of arrival predicted by the neural network for each signal. The functions: DoAAnalysis_neg90 and DoAAnalysis_pos90 are called by the DoAPrediction function, these functions create the neural network using the NN function, import the weights and biases, and passes the feature matrix (provided as input) through the neural network - the output of these functions are the predicted direction of arrival.</p> <p>MatLab files:</p> <p><br> 1.) runAnalysis.m - This MatLab script analyses the dataset provided as part of this repository. Users can change the variables head ('KEMAR' or 'KU100'), signalType ('directSound' or 'reflection'), and speaker ('EquatorD5' or 'Genelec8030'). This script will produce the gaussian normalised feature vector and expected direction of arrival for all signals with the defined head, signal type, and speaker combination. These variables are then saved in .mat files so they can be imported by the python scripts.</p> <p>2.) BinauralModelCochlea.m - This MatLab function analyses a given binaural signal and outputs the interaural cross-correlation, interaural level difference, interaural time difference, the cochlea output for the left and right channel and the centre frequencies of the gammatone filter band. The input variables are: IR - the signal to be analysed, N - the number of gammatone filters, freqLow - the lowest centre frequency of the gammatone filter bank (centre frequency of the first gammatone filter), and freqHigh - the highest centre frequency of the gammatone filter bank (the centre frequency of the Nth gammatone filter). This function requires Malcolm Slaney's Auditory Toolbox [1] and Bin Gao's Cochleagram function [2] in order to work.</p> <p>3.) generateFeatureVector.m - This MatLab function generates a feature vector from an input binaural signal x, and a version of the signal captured after the binaural dummy head has been rotated by either +90° or -90° degree (variables xPos90 and xNeg90 respectively). If the sampling frequency (Fs) isn't 44100, the signals are resampled to be at 44100. This file also contains a function 'gaussianNormalisationTestData' which gaussian normalises the data using the mean and standard deviation calculated from the data used to train the neural networks - the mean and standard deviation values are stored in the folder GMParams in the pos90 and neg90 folders.</p> <p>4.) generateTestData.m - This MatLab function analyses the included binaural dataset, it takes the input variables: head - the binaural dummy head used for the measurements either 'KEMAR' or 'KU100', speaker - the speaker used for the measurements either 'EquatorD5' or 'Genelec8030', and signalType - the type of signal being analysed either 'directSound' or 'reflection'.</p> <p>Text files:</p> <p><br> 1.) noLayers.txt - a text file containing the number of layers used when training the neural network - with the current version of the code the neural network contains only 1 layer.</p> <p>2.) README.txt - Read me file containing information about the repository.</p> <p>Audio files:</p> <p><br> This repository contains 1152 binaural signals half of which are direct sounds segmented from a binaural room impulse responses and the other half are reflections segmented from binaural room impulse responses (detailed in the paper this material supports) the direct sounds are recorded at angles from 0° to 357.5° in steps of 2.5° and the reflections are recorded at angles of 1° to 358.5° in steps of 2.5°. In the paper only recordings relating to signals recorded with the Equator D5 are analysed.</p> <p>The combination of audio files include:</p> <p>1.) 144 direct sound recordings captured with the KEMAR 45BC binaural dummy head microphone and the Equator D5 speaker<br> 2.) 144 reflection recordings captured with the KEMAR 45BC binaural dummy head microphone and the Equator D5 speaker<br> 3.) 144 direct sound recordings captured with the KU100 binaural dummy head microphone and the Equator D5 speaker<br> 4.) 144 reflection recordings captured with the KU100 binaural dummy head microphone and the Equator D5 speaker<br> 5.) 144 direct sound recordings captured with the KEMAR 45BC binaural dummy head microphone and the Genelec 8030 speaker<br> 6.) 144 reflection recordings captured with the KEMAR 45BC binaural dummy head microphone and the Genelec 8030 speaker<br> 7.) 144 direct sound recordings captured with the KU100 binaural dummy head microphone and the Genelec 8030 speaker<br> 8.) 144 reflection recordings captured with the KU100 binaural dummy head microphone and the Genelec 8030 speaker</p> <p>The files are stored using the following file naming convention:<br> head_Test3_speaker_signalType_000_0_Degrees.wav - where _000_0 defines the azimuth direction of arrival so for example for a direct sound measured with the KEMAR unit and the Genelec8030 at 5 degrees would be 'KEMAR_Test3_Genelec8030_directSound_005_0Degrees.wav' and for a reflection measured with the KU100 and the Equator D5 at 298.5 degrees would be 'KU100_Test3_EquatorD5_reflection_298_5Degrees.wav'</p> <p>--</p> <p>Bibliography:<br> [1] Slaney, M. (1998). Auditory Toolbox. Palo Alto, CA. [Online]. Available: https://engineering.purdue.edu/~malcolm/interval/1998-010/ [Accessed: Oct. 27, 2017]</p> <p>[2] Gao, B. (2014). Cochleagram and IS-NMF2D for Blind Source Separation. [Online] Available: http://uk.mathworks.com/matlabcentral/fileexchange/48622-cochleagram-and-is-nmf2d-for-blind-source-separation?focused=3855900&tab=function [Accessed: Oct. 27, 2017]</p> <p>[3] NumFocus. (n.d.). NumPy. [Online]. Available: http://www.numpy.org/ [Accessed: Oct. 27, 2017]</p> <p>[4] SciPy. (n.d.). SciPy. [Online]. Available: https://www.scipy.org/ [Accessed: Oct. 27, 2017]</p> <p>[5] Google. (n.d.). TensorFlow. [Online] Available: https://www.tensorflow.org/ [Accessed: Oct. 27, 2017]</p> <p>--</p> <p>All code and audio produced by: Michael Lovedee-Turner, PhD candidate in Music Technology at the Audio Lab, Department of Electronic Engineering, University of York</p> <p>Contact: mjlt500@york.ac.uk</p>
Extended Room Transition Dataset (ERTD) of room impulse responses in coupled rooms
<p>This dataset accompanies the publication</p> <blockquote> <div> <div> <div> <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> </div> </div> </div> </blockquote> <div> <div> <div> <p> </p> <div> <div> <div> <p>The dataset contains finite-difference time-domain (FDTD) simulations. It extends the Room Transition Dataset (RTD) by McKenzie et al (doi: <a href="https://doi.org/10.5281/zenodo.4636068" target="_blank" rel="noopener">10.5281/zenodo.4636068</a>). More precisely, it extends the “meeting room to hallway” transition, by additional receiver positions in both rooms. The ERTD contains 2833 RIRs in total. For all RIRs, the sound source position was chosen according to the “source in meeting room, no line-of-sight” (NLOS) configuration of the RTD. While the RTD only features receiver positions on a straight line between the rooms, the Extended Room Transition dataset samples the entire room geometry using a uniform grid with 0.2 m resolution. All simulations are performed on the horizontal plane at 1.55 m height.</p> <p>We simulated two variants of the dataset. In variant ERTD1, the wall absorption properties were assigned to approximately match the corresponding measurements. The absorption coefficient was assigned uniformly to all walls in the respective rooms, where the meeting room walls exhibited an absorption coefficient alphaR1 = 0.12, while the hallway was modeled less absorbent with alphaR2 = 0.042. In variant ERTD2, a significantly higher absorption coefficient alphaR3 = 0.48 is assigned to the right wall of the hallway, while all other surfaces are modeled analogous to ERTD1. We use ERTD2 in our evaluation to demonstrate that the common-slope model can also be used in environments with very non-uniform absorption distributions.</p> <p> </p> <p>___________</p> <div> <div> <div> <p>We acknowledge the computational resources provided by the Aalto Science-IT project.</p> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Anechoic McVAMPIRE – Anechoic Multichannel Varying Mouth Position Impulse Response Dataset
<p>This dataset contains impulse responses (IRs) that were recorded in an anechoic room. The recording setup imitates the geometry of a minivan with eight seats arranged in three seat rows. The IRs were captured with 14 overhead microphones positioned in the imaginary car roof using a built-in mouth simulator of a head and torso simulator (HATS) at eight passenger seat positions with eleven orientations each. In addition, the dataset contains IRs measured with four lateral loudspeakers imitating door loudspeakers, as well as a noise floor recording.</p> <p>This dataset supplements the <a href="https://doi.org/10.5281/zenodo.12806684">In-Car McVAMPIRE</a> dataset which was captured with an identical microphone setup in a real car. Both datasets can be used to simulate speech in a car from different seats with different speaker orientations including the loudspeaker-enclosure-microphone (LEM) system under anechoic or realistic, reverberant conditions.</p>
Volcanic Lightning and Continual Radio Frequency Impulses at Sakurajima Volcano: A Multiparametric Dataset
<p>This is a multiparametric data set of volcanic activity at Sakurajima volcano in Japan. The data set was collected in May and June 2015. The data set includes the following types of data: Lightning Mapping Array data, slow and fast electric field waveforms, log-RF VHF data, infrasound data, plume height, velocity, and temperature data.</p>
A multiple model high-resolution head-related impulse response database for aided and unaided ears (HDF5 format)
<p>The Multiple-Model High Resolution HRTF database is a collection of HRTFs measured using four different Head-and-Torso Simulators at high spatial resolution (2 degree azimuth and elevation). The data here is stored in HDF5 files, the SOFA files are published in a separate dataset <a href="https://zenodo.org/record/2582553">doi:10.5281/zenodo.2582553</a>.</p>
METU SPARG Eigenmike em32 Acoustic Impulse Response Dataset v0.1.0
<p><strong>DESCRIPTION</strong></p> <p>This dataset includes acoustic impulse response (AIR) measurements made using an Eigenmike em32 and the room impulse response measurements carried out at the same position using an Alctron M6 measurement microphone. The measurements were made in classroom S05 at the METU Graduate School of Informatics on 23 January 2018. </p> <p><strong>LICENSE</strong></p> <p>The dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).</p> <p><strong>MEASUREMENTS</strong></p> <p>The classroom in which the measurements were made has a high reverberation time (T60 ≈ 1.12 s) when empty. The room is approximately rectangular and has the dimensions 6.5 × 8.3 × 2.9 m. AIR measurements were made at 240 points on a rectilinear grid of 0.5 m horizontal and 0.3 m vertical resolution surrounding the array. The array was positioned at a height of 1.5 m. The measurement planes were positioned at the heights of 0.9, 1.2, 1.5, 1.8 and 2.1 m from the floor level. These positions cover the whole azimuth range and an elevation range of approximately ±50◦ above and below the horizontal plane.</p> <p>The sound source was a Genelec 6010A two-way loudspeaker whose acoustic axis pointed at the vertical axis of the array at all measurement positions. Logarithmic sine sweep method was used for the AIR measurements. </p> <p><strong>FILE FORMAT</strong></p> <p>The AIRs and RIRs are provided as 16-bit signed integer WAVE files. The sampling rate is 48 kHz.</p> <p><strong>NAMING CONVENTION</strong></p> <p>There are two folders: em32 and alctron. The former includes AIR measurements, and the latter includes the RIR measurements. Each of these folders include 244 subfolders where each subfolder is named as ABC, from 000 through to 664. See documentation.pdf for details.</p> <p>Note that the measurements right above and below the array are not ideal since the acoustic axis of the loudspeaker did not face the array. Therefore, these measurements are considered unfit and were not used in the publications given below.</p> <p><strong>HOW TO CITE</strong></p> <p>The dataset has the DOI number 10.5281/zenodo.2635758 and can be cited as:</p> <p><strong>Orhun Olgun, & Huseyin Hacihabiboglu. (2019). METU SPARG Eigenmike em32 Acoustic Impulse Response Dataset v0.1.0 (Version 0.1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2635758</strong></p> <p>The data presented here were used in one journal article and two conference papers as of the time of writing this document. Please also consider citing these papers if you find this dataset to be useful in your research:</p> <p>[1] Coteli, M. B., Olgun, O., and Hacihabiboglu, H. (2018). Multiple Sound Source Localization With Steered Response Power Density and Hierarchical Grid Refinement. IEEE/ACM Trans. Audio, Speech and Language Process., 26(11), 2215-2229.</p> <p>[2] Olgun, O. and Hacihabiboglu, H., (2018) "Localization of Multiple Sources in the Spherical Harmonic Domain with Hierarchical Grid Refinement and EB-MUSIC". In Proc. 2018 16th Int. Workshop on Acoust. Signal Enhancement (IWAENC-18) (pp. 101-105), Tokyo, Japan.</p> <p>[3] Coteli, M. B., and Hacihabiboglu, H., (2019), "Multiple Sound Source Localization with Rigid Spherical Microphone Arrays Via Residual Energy Test", Proc. 2019 IEEE Int. Conf. on Acoust., Speech and Signal Process., (ICASSP-19), Brighton, UK.</p>
BinauRec: A dataset to test the influence of the use of room impulse responses on binaural speech enhancement
<p>BinauRec is a dataset for binaural speech enhancement. It is composed of real recordings, measured and simulated room impulse responses for the same audio scenes. Measurements are realized using behind-the-ears hearing aid shells, with and without a dummy head.</p>
Ionization of sputtered material in high power impulse magnetron sputtering plasmas - comparison of titanium, chromium and aluminum
<p>Experimental data set and modeling results to an upcoming publication titled: "Ionization of sputtered material in high power impulse magnetron sputtering plasmas - comparison of titanium, chromium and aluminum".</p> <p>The dataset contains current and voltage measurements (current-voltage-XX.txt), Langmuir probe measurements (probe-XX.txt) performed 8 mm above the racetrack position (using the method described here https://doi.org/10.1088/1361-6595/ab5e46), model results as explained in the paper and spectroscopic imaging profiles. The spectroscopic imaging profiles are obtained from Abel-inverted images in the radial direction by integrating between z=1mm and z=3mm and in the axial direction between r=12mm and r=15mm.</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.