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opencc-by-4.0Apr 2016View details →

Binaural room impulse responses of an apartment-like environment

Measured Binaural Room Impulse Responses (BRIR) of the ADREAM Laboratory, LAAS-CNRS, Toulouse, France. The measurements are described in detail in this publication: 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 Abstract of the Publication: 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.

opencc-by-sa-4.0Oct 2016View details →

Binaural room impulse responses recorded with KEMAR of a 19-channel linear loudspeaker array

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.

Binaural room impulse responses recorded with KEMAR in a mid-size lecture hall

The binaural room impulse responses (BRIRs) were measured at the mid-size lecture room Auditorium 3 at the 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] [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

opencc-by-4.0Nov 2024View details →

Room Impulse Response Dataset: Niels Bohr Institute - Auditorium A

<h1>Dataset description</h1> 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 22nd & 23rd, 2023. The dataset includes: <ul> <li>rir_NBI_line.h5 – Sequential measurements over a line using a robotic arm (UR5, Universal Robots) for microphone positioning.</li> <li>rir_NBI_em32.h5 – Distributed RIRs measured with the 32-channel spherical microphone array Eigenmike (em32, mh Acoustics).</li> <li>rir_NBI_em32.sofa – SOFA format of the previous file.</li> </ul> <h2>The room</h2> 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. The reverberation time in octave bands is: <table> <tbody> <tr> <td> F (Hz) </td> <td> 32 </td> <td> 63 </td> <td> 126 </td> <td> 250 </td> <td> 500 </td> <td> 1k </td> <td> 2k </td> <td> 4k </td> <td> 8k </td> <td> 16k </td> </tr> <tr> <td> T30 (s) </td> <td> 0.75 </td> <td> 0.74 </td> <td> 0.61 </td> <td> 0.72 </td> <td> 0.80 </td> <td> 0.88 </td> <td> 1.03 </td> <td> 1.05 </td> <td> 0.85 </td> <td> 0.58 </td> </tr> </tbody> </table> <h2>The measurements</h2> 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. 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). The atmospheric conditions for each set of measurements are included in the corresponding file. <h1>Related publications</h1> This dataset is linked to the publication: <ul> <li>Figueroa-Duran A. & Fernandez-Grande E., Reconstruction of reverberant sound fields over large spatial domains. In J. Acoust. Soc. Am. (2025), <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> For any questions, please address them to anfig@dtu.dk.

opencc-by-4.0Nov 2021View details →

Dataset of Spatial Room Impulse Responses in a Variable Acoustics Room for Six Degrees-of-Freedom Rendering and Analysis

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.  Accompanying paper on details of the dataset measurement: https://arxiv.org/abs/2111.11882 Changelog: V 1.0 - Initial version. 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.

opencc-by-4.0Oct 2017View details →

Dataset for: Application of Machine Learning for the Spatial Analysis of Binaural Room Impulse Responses

This repository contains supplementary material for the paper titled `Application of Machine Learning for the Spatial 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.  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. The python scrips require the following Python libraries to be installed: Numpy[3], SciPy[4] and Tensorflow [5]. The MatLab code was tested using MatLab R2017a on a Computer running windows 7. The python code was tested using Python 3.2.5, using an anaconda Python environment - in windows command line. -- The repository contains: Folders: 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. 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. 3.) testData - this folder contains pre-generated test data for the different binaural dummy head microphones, speaker, and signal type combinations. Python Scripts: 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 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. MatLab files: 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. 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. 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. 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'. Text files: 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. 2.) README.txt - Read me file containing information about the repository. Audio files: 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. The combination of audio files include: 1.) 144 direct sound recordings captured with the KEMAR 45BC binaural dummy head microphone and the Equator D5 speaker 2.) 144 reflection recordings captured with the KEMAR 45BC binaural dummy head microphone and the Equator D5 speaker 3.) 144 direct sound recordings captured with the KU100 binaural dummy head microphone and the Equator D5 speaker 4.) 144 reflection recordings captured with the KU100 binaural dummy head microphone and the Equator D5 speaker 5.) 144 direct sound recordings captured with the KEMAR 45BC binaural dummy head microphone and the Genelec 8030 speaker 6.) 144 reflection recordings captured with the KEMAR 45BC binaural dummy head microphone and the Genelec 8030 speaker 7.) 144 direct sound recordings captured with the KU100 binaural dummy head microphone and the Genelec 8030 speaker 8.) 144 reflection recordings captured with the KU100 binaural dummy head microphone and the Genelec 8030 speaker The files are stored using the following file naming convention: 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' -- Bibliography: [1] Slaney, M. (1998). Auditory Toolbox. Palo Alto, CA. [Online]. Available: https://engineering.purdue.edu/~malcolm/interval/1998-010/ [Accessed: Oct. 27, 2017] [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] [3] NumFocus. (n.d.). NumPy. [Online]. Available: http://www.numpy.org/ [Accessed: Oct. 27, 2017] [4] SciPy. (n.d.). SciPy. [Online]. Available: https://www.scipy.org/ [Accessed: Oct. 27, 2017] [5] Google. (n.d.). TensorFlow. [Online] Available: https://www.tensorflow.org/ [Accessed: Oct. 27, 2017] -- 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 Contact: mjlt500@york.ac.uk

opencc-by-4.0Jun 2024View details →

Extended Room Transition Dataset (ERTD) of room impulse responses in coupled rooms

This dataset accompanies the publication <blockquote> <div> <div> <div> 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> </div> </div> </div> </blockquote> <div> <div> <div>   <div> <div> <div> 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. 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.   ___________ <div> <div> <div> We acknowledge the computational resources provided by the Aalto Science-IT project. </div> </div> </div> </div> </div> </div> </div> </div> </div>

opencc-by-4.0Nov 2022View details →

BinauRec: A dataset to test the influence of the use of room impulse responses on binaural speech enhancement

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.

opencc-by-4.0Apr 2016View details →

Binaural room impulse responses of a 5.0 surround setup for different listening positions

Binaural Room Impulse Responses - KEMAR, room Calypso, TU Berlin, 5.0 Surround setup   This dataset contains binaural room impulse responses (BRIRs) measured at nine different listening positions for a 5.0 surround setup in the listening room Calypso in the Telefunken building of Technische Universität Berlin, Berlin, Germany. The room has a volume of 83 m³ and a reverberation time RT60 of 0.17 s at a frequency of 1 kHz. "doc.zip" contains additional information for the measurement, "*.sofa" are the actual BRIRs, one file for every listening position, where the position is indicated by the X*-Y*-values. In order to work with those files you need a SOFA API for your programming language. For example, the one for Matlab can be found here: https://github.com/sofacoustics/API_MO/releases/latest If you want to create a single SOFA file containing all listening positions, you can execute the "combine_positions.m" script in Matlab after installing and starting the SOFA API. The BRIRs were with a head-orientation varying in the range of +-90° with 1° resolution. Room shape and size, listener and sound source positions are shown in "setup_calypso_surround_genelec8030A.pdf". Directory "./photos" contains photographs of the measurement setup. Copyright 2016 Hagen Wierstorf Licensed under Creative Commons (CC-BY-4.0)

opencc-by-4.0Apr 2016View details →

Binaural room impulse responses of a 5.0 surround setup for different listening positions

This dataset contains binaural room impulse responses (BRIRs) measured at nine different listening positions for a 5.0 surround setup in the listening room Calypso in the Telefunken building of Technische Universität Berlin, Berlin, Germany. The room has a volume of 83 m³ and a reverberation time RT60 of 0.17 s at a frequency of 1 kHz. The measurment was done with Genelec 8030A loudspeakers and repeated for the central listening positon with the larger Genelec 8250A loudspeakers. "doc.zip" contains additional information for the measurement, "*.sofa" are the actual BRIRs, one file for every listening position, where the position is indicated by the X*-Y*-values. The file `KEMAR_Calypso_Surround.sofa` contains all listening positions in one file. In order to work with those files you need a SOFA API for your programming language. For example, the one for Matlab can be found here: https://github.com/sofacoustics/API_MO/releases/latest The BRIRs were with a head-orientation varying in the range of +-90° with 1° resolution. Room shape and size, listener and sound source positions are shown in `setup_calypso_surround_genelec8030A.pdf` and `setup_calypso_surround_genelec8250A.pdf`. Directory "./photos" contains photographs of the measurement setup. Copyright 2016 Hagen Wierstorf Licensed under Creative Commons (CC-BY-4.0)

Binaural room impulse responses: Same listener-source-setup at different positions in the room

To study the perception of room acoustics in dependency of the position in the room, measurements with KEMAR head-and-torso-simulator were conducted. The dummy head was placed at 5 different positions in a small conference room (10.3mx5.8mx3.1m, RT=0.65s). The source, a loudspeaker Genelec 1030A, was always positioned in the same relation to the listening position. BRIRs were measured with an azimuth-resolution of 5° from 0°-360°. This data allows a psychoacoustical comparison of the room acoustical properties at different positions in the room.

Binaural room impulse responses: Same listener-source-setup at different positions in the room

Binaural room impulse responses recorded with KEMAR in a small meeting room

The binaural room impulse responses (BRIRs) were measured in the small meeting room Spirit at the Telefunken-building of TU Berlin. They were measured for three different loudspeaker positions placed around a table. 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] [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

opencc-by-4.0Jun 2021View details →

MeshRIR: Dataset of Room Impulse Responses on Meshed Grid Points

MeshRIR is a dataset of acoustic room impulse responses (RIRs) at finely meshed grid points. Two subdatasets are currently available: one consists of IRs in a 3D cuboidal region from a single source, and the other consists of IRs in a 2D square region from an array of 32 sources. This dataset is suitable for evaluating sound field analysis and synthesis methods.  See the link below for the details: <a href="https://sh01k.github.io/MeshRIR/">https://sh01k.github.io/MeshRIR/</a>

opencc-by-4.0May 2022View details →

A dataset of measured spatial room impulse responses in different rooms including visualization

An open-source dataset of captured spatial room impulse responses (SRIRs) is presented. The data was collected in different enclosed spaces at the Technische Universität Ilmenau using an open self-build microphone array design following the spatial decomposition method (SDM) guidelines. The included rooms were selected based on their distinctive acoustical properties resulting from their general build and furnishing as required by their utility. Three different classes of spaces can be distinguished, including seminar rooms, offices, and classrooms. For each considered space different source-receiver positions were recorded, including 360° images for each condition. The dataset can be utilized for various augmented or virtual reality applications, using either a loudspeaker or headphone-based reproduction alongside the appropriate head-related transfer function sets. In future, we plan to add more rooms and more source-receiver positions. Please cite our corresponding paper: Klein, F., Surdu, T., Aretz, A., Birth, K., Edelmann, N., Seitelman, F., Ziener, C., Werner, S., and Sporer, T., “A dataset of measured spatial room impulse responses in different rooms including visualization,” in 152nd AES Convention, 2022, https://www.aes.org/e%E2%80%90lib/browse.cfm?elib=21728

opencc-by-4.0Jun 2024View details →

Multi-Purpose Room Impulse Response Dataset Measured on a 3D Spatial Grid

<h1>Introduction</h1> The sound field inside a room depends on many factors, such as the room shape, the absorption characteristics of the materials that comprise the bounding surfaces, the furniture present in the room, and the source position and its acoustic characteristics. An increasing number of publicly available room impulse response (RIR) databases that aim to provide detailed descriptions of interior sound fields can be found in the literature. These databases can be utilized in research as well as in the development and verification of signal processing algorithms that use this information on the acoustic environment. The availability of many RIR databases covering diverse scenarios is beneficial to the community. We provide a database of RIRs, namely the Multi-Purpose RIR (MP-RIR) dataset, which contains 68736 RIRs measured on a dense 3D grid inside a complex-shaped room. We used a measurement robot with a rotating arm that operates as a linear guide and is capable of moving a vertical, linear array of eight omni-directional microphones. Four different sources have been used and were placed at eight different positions inside the room. A detailed desciption of the measurement campaign and the dataset is presented in the paper (https://aes2.org/publications/elibrary-page/?id=22515).  <h1>Contents of the MP-RIR dataset</h1> In the following, the contents and the structure of the provided dataset are described: <ul> <li>Sk_Mrir.npy: Matrix, which contains the RIRs for all measured grid points for the loudspeaker Sk, k = 1, 2, ..., 8. The matrix has the shape [N_xy, N_z, N] = [1074 x 8 x 100096], where N_xy is the number of 2D grid positions to which the robot is moving the vertical microphone array of N_z microphones. The length of each RIR is described by N.</li> <li>Mxyz.npy: Matrix, which contains the microphone coordinates of the measured RIRs and corresponds to the matrices Sk_Mrir. The matrix has the shape [N_xy, N_z, N_d] = [1074 x 8 x 3]. The indexing for the first two dimensions is the same as for the matrices Sk_Mrir, so that the microphone coordinates can be immediately retrieved for the provided RIRs. The third dimension with the length N_d gives access to the x-, y- and z-coordinate values in meters.</li> <li>Setup.npz Dictionary, which contains parameters related to the measurement setup, with the following keys: <ul> <li>angles_speaker Dictionary of azimuth angles in degrees of the loudspeakers, with the keys S1, S2, ..., S8.</li> <li>coord_speaker_center Dictionary, which contains the x-, y- and z-coordinates of the loudspeaker positions at the center of the base of each loudspeaker. The coordinate arrays can be accessed with the keys S1, S2, ..., S8.</li> <li>coord_polygon Array of shape [4,2], which contains the x- and y-coordinates in meters of the room corners C_q, q=0,1,2,3. The first dimension of the array relates to the room corners and the second dimension relates to the coordinates. </li> <li>fs Sampling rate in Hz.</li> <li>T_guard Guard time in samples. The guard time provides additional samples at the beginning of the RIR to increase the quality of the RIR.</li> <li>T_system Delay of the measurement system in samples.</li> </ul> </li> </ul> <h1>Further Information</h1> The delay of the RIRs is composed of the guard time T_guard, the system delay T_system and the acoustic delay T_ac. The guard time and system delay can be retrieved from the file Setup.npz described above. A gain alignment procedure was applied to align the output SPL between the loudspeakers, as described in the paper. Additionally, all RIRs were scaled by the same value, the maximum absolute peak of all measured RIRs. As a result, the maximum absolute value in each individual RIR is less or equal to 1.

opencc-by-4.0Jun 2024View details →

Room Impulse Responses for Low-Frequency Sound Field Control

<h2>About</h2> <div> <div> <div> A dataset of room impulse responses (RIRs) measured in the low frequency range, for different measurement signal lengths, in two rooms with different acoustic conditions. Acquired with the purpose of low-frequency sound zones rendering and evaluation, the dataset can be used in general for different sound field control methods. By design, the dataset is composed of two sets of RIRs: one intended for the design of the control strategies, and another one intended for evaluation [1]. The RIRs of the first set, obtained with measurement signals of different length, allow exploring the influence of the acquisition time of the RIRs in the control methods [2]. The RIRs of the second set allow evaluating the sound field generated at and around the position of the RIRs of the first set. The RIRs were acquired with the Synchronized Swept-Sine (SSS) method, proposed by Novak et al. [3], at a samplig frequency of 48 kHz with SSS signals varying from 15 Hz to 600 Hz. The obtained RIRs were re-sampled to 1.2 kHz. Two files with different contents have been added: <ul> <li>RIR_LF_SFC_Light: contains the documentation, MatLab codes, and the ready-to-use RIRs stored as 3D-arrays in .mat files. </li> <li>RIR_LF_SFC_Full: in addition to files in RIR_LF_SFC_Light, it contains the original SSS signals and the signals recorded during the measurements. These were used to retrieve the RIRs and therefore, can be used for custom purposes.  </li> </ul> </div> </div> </div> <div> <h2>Citation</h2> Please cite the database with the following paper: @inproceedings{cadavid_ATvsSS_2024, title={Spatial Sampling versus Acquisition Time of Room Impulse   Responses for Low-Frequency Sound Zones},   author={Cadavid, Jos{\'e} and M{\o}ller, Martin Bo and van Waterschoot, Toon and Bech, S{\o}ren and {\O}stergaard, Jan},   booktitle={Audio Engineering Society Convention 156},   year={2024},   organization={Audio Engineering Society} } <h2>Acknowledgements</h2> The authors would like to thank <a href="https://orcid.org/0000-0002-1452-2227" target="_blank" rel="noopener">Antonin Novak</a>, <a href="https://orcid.org/0000-0002-2175-6603" target="_blank" rel="noopener">Christian S. Pedersen</a>, and Claus Vestergaard for their help with the RIRs measurements. This project has received funding from the European Union’s (EU) Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions Grant No. 956369. </div>