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12 results for “Spatial Impulse Responses”

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zenodo48/100

Open Database of Spatial Room Impulse Responses at Detmold University of Music

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:  <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> 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). 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. If you use this dataset for your research, please cite our work: Amengual Gari, S. V.; Sahin, B.; Eddy, D; Kob, M.: "Open Database of Spatial Room Impulse Responses at Detmold University of Music", 149th Convention of the Audio Engineering Society, 2020.   The database is organized in 3 sets: - Set A: Source: Single Source measurements. Receiver: Open Array and Dummy Head. Rooms: BS, DST, KH Special configurations: Artificial reverberation, music stand on stage - Set B:  Source: Loudspeaker and WFS orchestra Receiver: Open Array. Rooms: KH - Set C: Source: Loudspeaker orchestra Receiver: Dummy Head and Omni8 array Rooms: KH   Further details on the measurement procedure and acoustical analysis of the RIRs can be found in the following publications: Set A Amengual Gari, S. V., Investigations on the Influence of Acoustics on Live Music Performance using Virtual Acoustic Methods, Ph.D. thesis, 2017. 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. Set B 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. Set C Sahin, B., ““Investigation of the Detmold Concert Hall auditorium acoustics by comparing preference ratings and objective measurements.”, M.Sc. Thesis, 2017. 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.

opencc-by-4.0Aug 2020View details →

zenodo44/100

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-4.0Nov 2021View details →

zenodo44/100

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-4.0Oct 2017View details →

zenodo40/100

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-4.0May 2022View details →

zenodo40/100

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 →

zenodo40/100

Spatial Room Impulse Response Dataset: A Robot's Journey Through Coupled Rooms of a Reverberant University Building

This is a dataset of Spatial Room Impulse Responses obtained by a robot equipped with a microphone array. The measurements were conducted in a reverberant university building, the Helmholtz building at Technische Universität Ilmenau (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. 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: <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> As an example "Plot Reverberation Time.jpg" shows the reverberation times for all measured positions of Area 1 and 2 with speaker HM.

opencc-by-4.0Feb 2024View details →

zenodo40/100

A High-Resolution Spatial Room Impulse Response Database

A High-Resolution Spatial Room Impulse Response Database: 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.  Naming Convention: Receivers: SMA (DRIRs): spherical microphone array impulse responses on a 2702 sampling point Lebedev grid.                    KU100 (BRIRs): Neumann KU100 dummy head impulse responses on a 360 sampling point horizontal grid                    Omni_ir: Omnidirectional impulse responses measured with an Earthworks M30 microphone  Receiver positions: P1, P2 as indicated in the floor plans Source positions: LSL: left speaker, LSR: right speaker, LSC: center speaker as indicated in the floor plans  Rooms: Audiolab, Classroom, Audimax _______________________________________________________________________________________ Contact: Tim Lübeck, Johannes M. Arend, and Christoph Pörschmann TH Köln - University of Applied Sciences Institute of Communications Engineering Department of Acoustics and Audio Signal Processing Betzdorfer Str. 2, D-50679 Cologne, Germany <a href="https://www.th-koeln.de/personen/tim.luebeck/">https://www.th-koeln.de/personen/tim.luebeck/</a> <a href="https://www.th-koeln.de/personen/johannes.arend/">https://www.th-koeln.de/personen/johannes.arend/</a> <a href="http://www.th-koeln.de/personen/christoph.poerschmann/">https://www.th-koeln.de/personen/christoph.poerschmann/</a>

opencc-by-4.0Aug 2021View details →

zenodo36/100

TAU Spatial Room Impulse Response Database (TAU-SRIR DB)

DESCRIPTION The TAU Spatial Room Impulse Response Database (TAU-SRIR DB) 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: <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> 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>. NOTE: 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.   REPORT AND REFERENCE A compact description of the dataset, recording setup, recording procedure, and extraction can be found in: Politis., Archontis, Adavanne, Sharath, & Virtanen, Tuomas (2020). A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020), Tokyo, Japan. 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.   AIM 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: <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>   SPECIFICATIONS 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. 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: <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> 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 close and a far 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, close and far, 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. Each trajectory group had multiple measurement trajectories, following the same floor path, but with the source at different heights.  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. More details on the trajectory geometries can be found in the README file and the measinfo.mat file.   RECORDING FORMATS 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.  We intend to add additional formats of the database, of both higher resolution (e.g. higher-order Ambisonics), or lower resolution (e.g. binaural).   REFERENCE DOAs 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. The DOAs are provided as Cartesian components [x, y, z] of unit length vectors.   SCENE GENERATOR A set of routines is shared, here termed scene generator, 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">TAU-NIGENS Spatial Sound Events 2021</a> dataset, and has been ported to Python from the original version written in Matlab. The generator can be found [**here**](https://github.com/danielkrause/DCASE2022-data-generator), along with more details on its use.  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. The dataset together with the generator has been used by the authors in the following public challenges: - <a href="https://dcase.community/challenge2019/task-sound-event-localization-and-detection">DCASE 2019 Challenge Task 3</a>, to generate the TAU Spatial Sound Events 2019 dataset (<a href="https://doi.org/10.5281/zenodo.2599196">development</a>/<a href="https://doi.org/10.5281/zenodo.3377088">evaluation</a>) - <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">TAU-NIGENS Spatial Sound Events 2020</a> dataset - <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">TAU-NIGENS Spatial Sound Events 2021</a> dataset - <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">SELD synthetic mixtures for training the task baseline</a> NOTE: 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.   DATASET STRUCTURE The dataset contains a folder of the SRIRs (TAU-SRIR_DB), with all the SRIRs per room in a single MAT file. The file rirdata.mat contains some general information such as sample rate, format specifications, and most importantly the DOAs of every extracted SRIR. The file measinfo.mat contains measurement and recording information in each room. Finally, the dataset contains a folder of spatial ambient noise recordings (TAU-SNoise_DB), 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.   DOWNLOAD The files TAU-SRIR_DB.z01, ..., TAU-SRIR_DB.zip contain the SRIRs and measurement info files. The files TAU-SNoise_DB.z01, ..., TAU-SNoise_DB.zip contain the ambient noise recordings. 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: Combine the split archive to a single archive: <pre><code>zip -s 0 split.zip --out single.zip</code></pre> Extract the single archive using unzip: <pre><code>unzip single.zip</code></pre>   LICENSE The database is published under a custom **open non-commercial with attribution** license. It can be found in the `LICENSE.txt` file that accompanies the data.

openother-ncApr 2022View details →

zenodo32/100

A Spatial Audio Impulse Response Compilation Captured at the WDR Broadcast Studios

[1] P. Stade, B. Bernschütz, and M. Rühl, “A Spatial Audio Impulse Response Compilation Captured at the WDR Broadcast Studios,” in Proceedings of the 27th Tonmeistertagung - VDT International Convention, 2012, pp. 1–17. Download link: <a href="http://audiogroup.web.th-koeln.de/FILES/VDT2012_WDRIRC.pdf">Conference Paper TMT2012, Cologne (Germany)</a> [2] B. Bernschütz, “Sound Field Analysis in Room Acoustics,” in Proceedings of the 27th Tonmeistertagung - VDT International Convention, 2012, pp. 1–22. Download link: <a href="http://audiogroup.web.th-koeln.de/FILES/VDT2012_SFARA.pdf">Conference Paper TMT2012, Cologne (Germany)</a> Files also available at <a href="https://www.sofaconventions.org/mediawiki/index.php/Files">sofaconventions.org</a> in SOFA file format. _______________________________________________________________________________________________________ Binaural room impulse responses (BRIRs) and spherical microphone array impulse responses (DRIRs) of the WDR broadcast studios in Cologne in SOFA file format. The BRIRs were measured with a Neumann KU100 dummy head for 360 directions along the horizontal plane (1° spatial resolution). The DRIRs were measured for rigid- and open-sphere configurations on different Lebedev grids. For further details, please refer to the conference publications listed below: ________________________________________________________________________________________________________ Contact: Christoph Pörschmann TH Köln - University of Applied Sciences Institute of Communications Engineering Department of Acoustics and Audio Signal Processing Betzdorfer Str. 2, D-50679 Cologne, Germany <a href="https://www.th-koeln.de/akustik">https://www.th-koeln.de/akustik</a> _________________________________________________________________ Naming Convention (please see name_coding_chart.pdf for more details): LBS: Large Broadcast Studio (Großer Sendesaal / Klaus-von-Bismarck-Saal) SBS: Small Broadcast Studio (Kleiner Sendesaal) CR1: Control Room 1 CR7: Control Room 7 KU: Neumann KU100 Dummy Head VSA: VariSphear Microphone Array MIC: (Stereo) Microphones

opencc-by-4.0May 2020View details →

zenodo32/100

HOMULA-RIR: A Room Impulse Response Dataset for Teleconferencing and Spatial Audio Applications Acquired Through Higher-Order Microphones and Uniform Linear Microphone Arrays

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.

opencc-by-4.0Jan 2024View details →

zenodo32/100

360° Binaural Room Impulse Response (BRIR) Database for 6DOF spatial perception research

by Applied Psychoacoustics Lab, University of Huddersfield   Authors: Bogdan Bacila and Hyunkook Lee bogdan.bacila@hud.ac.uk, h.lee@hud.ac.uk   Description 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.   Folder Structure The impulse responses are organised into two main folders: * Binaural: Contains the SOFA files and MATLAB files for each position, with a 3.6° angular resolution, recorded with the Neumann KU100 binaural head. * 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.     Naming Convention The files are named after their relative position on the stage and the distance from the stage: * C = Centre * L = Left * LW = Left Wide   License This project is licensed under the CC-BY-4.0 License - see the License.txt file for details   Publication This database was presented at the Audio Engineering Society 146th International Convention. Download link: http://www.aes.org/e-lib/browse.cfm?elib=20371   Referencing If you use the database for your research, please reference it as follows. 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

opencc-by-4.0Mar 2019View details →

zenodo32/100

A dataset of measured spatial room impulse responses for the transition between coupled rooms

For a detailed description of the measurement and analysis methods, please see: McKenzie, T., Schlecht, S. J., and Pulkki, V. (2021). Acoustic Analysis and Dataset of Transitions between Coupled Rooms. IEEE International Conference on Acoustics, Speech and Signal Processing.   This dataset contains measured spatial room impulse responses for the transition between coupled rooms. Four coupled room pairs are included: <ul> <li>Meeting Room to Hallway</li> <li>Office to Anechoic Chamber</li> <li>Office to Kitchen</li> <li>Office to Stairwell</li> </ul> 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.  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.  Changelog:  V 1.0 - Initial version V 1.1 - Added plots only download option V 1.2 - Improved time alignment of impulse responses and changed normalisation to a single value relative to the maximum of the entire dataset 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.  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.

opencc-by-4.0Feb 2021View details →

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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Last verified 2026-04-30Open record

International Brain Laboratory public data

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OpenNeuro

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Last verified 2026-04-29Open record