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89 results for “Impulse Response”
Reciprocal SEM simulations of seismic impulse response at Piton de la Fournaise volcano
<p>This Dataset contains synthetic seismic signals simulated using the Spectral Element Method (SEM) for the study of rockfall seismic signals at Piton de la Fournaise volcano. Synthetic signals from both a reference model with flat surface and a model with Dolomieu crater surface topography of 10m resolution are contained.</p> <p>The simulations were carried out reciprocally for seismometers BON, BOR, DSO and SNE, implementing surface point force in form of a 7Hz Ricker wavelet directed vertically ('vertical_Z'), eastwards ('horizontal_E'), and northwards ('horizontal_N').</p> <p>The respective source position is stored in 'source.txt' and the measurement positions in 'stations.txt'. The 'traces'-folder contains a file corresponding to each station with 7 columns including simulation time t, ground displacement s and ground velocity v:<br> Time t (s) | East s_X (m) | North s_Y (m) | Vertical s_Z (m)| East v_X (m/s) | North v_Y (m/s) | Vertical v_Z (m/s)</p> <p>Sampling frequency: 100Hz</p>
A Spatial Audio Impulse Response Compilation Captured at the WDR Broadcast Studios
<p>[1] P. Stade, B. Bernschütz, and M. Rühl, “A Spatial Audio Impulse Response Compilation Captured at the WDR Broadcast Studios,” in <em>Proceedings of the 27th Tonmeistertagung - VDT International Convention</em>, 2012, pp. 1–17.<br> Download link: <a href="http://audiogroup.web.th-koeln.de/FILES/VDT2012_WDRIRC.pdf">Conference Paper TMT2012, Cologne (Germany)</a></p> <p>[2] B. Bernschütz, “Sound Field Analysis in Room Acoustics,” in <em>Proceedings of the 27th Tonmeistertagung - VDT International Convention</em>, 2012, pp. 1–22.<br> Download link: <a href="http://audiogroup.web.th-koeln.de/FILES/VDT2012_SFARA.pdf">Conference Paper TMT2012, Cologne (Germany)</a></p> <p>Files also available at <a href="https://www.sofaconventions.org/mediawiki/index.php/Files">sofaconventions.org</a> in SOFA file format.</p> <p>_______________________________________________________________________________________________________</p> <p>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:</p> <p>________________________________________________________________________________________________________</p> <p><strong>Contact:</strong><br> Christoph Pörschmann<br> TH Köln - University of Applied Sciences<br> Institute of Communications Engineering<br> Department of Acoustics and Audio Signal Processing<br> Betzdorfer Str. 2, D-50679 Cologne, Germany<br> <a href="https://www.th-koeln.de/akustik">https://www.th-koeln.de/akustik</a></p> <p>_________________________________________________________________</p> <p><strong>Naming Convention (please see name_coding_chart.pdf for more details):</strong></p> <p><strong>LBS</strong>: Large Broadcast Studio (Großer Sendesaal / Klaus-von-Bismarck-Saal)</p> <p><strong>SBS:</strong> Small Broadcast Studio (Kleiner Sendesaal)</p> <p><strong>CR1: </strong>Control Room 1</p> <p><strong>CR7</strong>: Control Room 7</p> <p><strong>KU: </strong>Neumann KU100 Dummy Head</p> <p><strong>VSA</strong>: VariSphear Microphone Array</p> <p><strong>MIC</strong>: (Stereo) Microphones</p>
DRR-scaled Individual Binaural Room Impulse Responses
<p>Dataset of recorded individual binaural room impulse responses (BRIRs). The BRIRs are scaled their Direct-to-Reverberant-Energy-Ratio (DRR). The DRR is changed by an amplification or damping of the reverberant part relative to the direct sound of a measured BRIR. The change of the BRIR is conducted 3 ms after the direct sound, avoiding effects on the head-related part. The DRRs are calculated for 70 steps with 46 damping steps at normalized amplitude from zero to one, 23 amplification steps at normalized amplitude from one to 1.3, and the original DRR.</p>
Database of simulated impulse responses and a scene constructor framework for the Audiovisual Immersion Lab (AVIL) at DTU
<p>This package contains a set of simulated impulse responses (IRs) intended to represent low, mid, and high reverberation environments, originally developed for the Cognitive Control of a Hearing Aid (<a href="https://cocoha.org" target="_blank" rel="noopener">COCOHA</a>) project, and rendered to be reproduced in the Audiovisual Immersion Lab (AVIL) at the Technical University of Denmark (DTU). </p> <p>A MATLAB-based scene constructor framework is also provided, which allows the generation of acoustic scenes based on the supplied IR databases with any input signal, as well as providing a means to generate binaural output signals.</p> <p>The impulse responses are provided as <a href="https://www.sofaconventions.org/mediawiki/index.php/SOFA_(Spatially_Oriented_Format_for_Acoustics)" target="_blank" rel="noopener">SOFA</a> (Spatially Oriented Format for Acoustics) files, including metadata about source and receiver positions, and require installation of the <a href="https://sourceforge.net/projects/sofacoustics/" target="_blank" rel="noopener">MATLAB SOFA API.</a></p>
HOMULA-RIR: A Room Impulse Response Dataset for Teleconferencing and Spatial Audio Applications Acquired Through Higher-Order Microphones and Uniform Linear Microphone Arrays
<p>In this paper, we present HOMULA-RIR, a dataset of room impulse responses (RIRs) acquired using both higher-order microphones (HOMs) and a uniform linear array (ULA), in order to model a remote attendance teleconferencing scenario. Specifically, measurements were performed in a seminar room, where a 64-microphone ULA was used as a multichannel audio acquisition system in the proximity of the speakers, while HOMs were used to model 25 attendees actually present in the seminar room. The HOMs cover a wide area of the room, making the dataset suitable also for applications of virtual acoustics. Through the measurement of the reverberation time and clarity index, and sample applications such as source localization and separation we demonstrate the effectiveness of the HOMULA-RIR dataset.</p>
FOA-MEIR Dataset: multi-environment impulse response recordings with a first-order ambisonic microphone
<p>FOA-MEIR is an impulse response (IR) dataset recorded in over 100 environments for use in sound event localization and detection (SELD) tasks. This dataset is set up to develop a robust SELD system in an unknown environment, and the IRs for the inferred environment are recorded at a different location from that of training data. The dataset also contains dry source recordings that can be combined with IR recordings to generate audio clips for training the SELD task.</p> <p>License: see the file named LICENSE.pdf</p> <p>Further information is available at [1] and Github: https://github.com/nttrd-mdlab/seld-foa-meir<br> <br> [1] Masahiro Yasuda, Yasunori Ohishi, Shoichiro Saito, “Echo-aware Adaptation of Sound Event Localization and Detection in Unknown Environments,” in IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), 2022.</p>
Synaptic currents through Purkinje cells in response to a single impulse in the granular layer
<p>Synaptic currents through Purkinje cells in response to a single impulse in the granular layer</p>
CT-AudioLink : Room Impulse Responses - http://ct-audiolink.gr
<p>Room Impulse responses measured during the project CT - Audiolink. The responses correspond to various measuring positions at cultural heritage buildings of Thrace region. These are:</p> <p>AKA = Kuyumjoglou Museum<br> EP = Church of Panagia<br> GJ = Yenni Mosque<br> IM = Imaret of Komotini ( Room A = IMA , Room B = IMB )<br> </p>
TUT Sound Events 2018 - Circular array, Reverberant and Synthetic Impulse Response Dataset
<p><strong>Tampere University of Technology (TUT) Sound Events 2018 - Circular array, Reverberant and Synthetic Impulse Response Dataset</strong></p> <p>This dataset consists of simulated, reverberant, and circular-array format recordings with stationary point sources each associated with a spatial coordinate. The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), spatial location in azimuth and elevation angles (in degrees), and distance from the microphone (in meters). The sound events are spatially placed within a room using the image source method. The room size chosen was 10x8x4 meter with reverberation time per octave band of [1.0, 0.8, 0.7, 0.6, 0.5, 0.4] s and 125 Hz–4 kHz band center frequencies.</p> <p>The isolated sound events were taken from the <a href="https://archive.org/details/dcase2016_task2_train_dev">DCASE 2016 task 2 dataset.</a> This dataset consists of 11 sound event classes such as Clearing throat, Coughing, Door knock, Door slam, Drawer, Human laughter, Keyboard, Keys (put on a table), Page turning, Phone ringing and Speech. The sound events are randomly placed in a spatial grid with 10-degree resolution in full azimuth and [-60 60) degree elevation angles. Additionally, the sound events are placed at a random distance of at least 1 meter away from the microphone.</p> <p>The license of the dataset can be found in the LICENSE file. The rest of the nine zip files consists of datasets for a given split and overlap. For example, the ov3_split1.zip file consists of the audio and metadata folders for the case of maximum three temporally overlapping sound events (ov3) and the first cross-validation split (split1). Within each audio/metadata folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Sound event localization and detection of overlapping sources using convolutional recurrent neural network</a>' work.</p>
TUT Sound Events 2018 - Ambisonic, Anechoic and Synthetic Impulse Response Dataset
<p><strong>Tampere University of Technology (TUT) Sound Events 2018 - Ambisonic, Anechoic, and Synthetic Impulse Response Dataset </strong></p> <p>This dataset consists of simulated anechoic first order Ambisonic (FOA) format recordings with stationary point sources each associated with a spatial coordinate. The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), spatial location in azimuth and elevation angles (in degrees), and distance from the microphone (in meters).</p> <p>The isolated sound events were taken from the <a href="https://archive.org/details/dcase2016_task2_train_dev">DCASE 2016 task 2 dataset.</a> This dataset consists of 11 sound event classes such as Clearing throat, Coughing, Door knock, Door slam, Drawer, Human laughter, Keyboard, Keys (put on a table), Page turning, Phone ringing and Speech. The sound events are randomly placed in a spatial grid with 10-degree resolution in full azimuth and [-60 60) degree elevation angles. Additionally, the sound events are placed at a random distance of [1 10] meters from the microphone.</p> <p>The license of the dataset can be found in the LICENSE file. The rest of the nine zip files consists of datasets for a given split and overlap. For example, the ov3_split1.zip file consists of the audio and metadata folders for the case of maximum three temporally overlapping sound events (ov3) and the first cross-validation split (split1). Within each audio/metadata folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Sound event localization and detection of overlapping sources using convolutional recurrent neural network</a>' work.</p>
TUT Sound Events 2018 - Circular array, Anechoic and Synthetic Impulse Response Dataset
<p><strong>Tampere University of Technology (TUT) Sound Events 2018 - Circular array, Anechoic and Synthetic Impulse Response Dataset</strong></p> <p>This dataset consists of simulated anechoic circular-array format recordings with stationary point sources each associated with a spatial coordinate. The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), spatial location in azimuth and elevation angles (in degrees), and distance from the microphone (in meters).</p> <p>The isolated sound events were taken from the <a href="https://archive.org/details/dcase2016_task2_train_dev">DCASE 2016 task 2 dataset.</a> This dataset consists of 11 sound event classes such as Clearing throat, Coughing, Door knock, Door slam, Drawer, Human laughter, Keyboard, Keys (put on a table), Page turning, Phone ringing and Speech. The sound events are randomly placed in a spatial grid with 10-degree resolution in full azimuth and [-60 60) degree elevation angles. Additionally, the sound events are placed at a random distance of [1 10] meters from the microphone.</p> <p>The license of the dataset can be found in the LICENSE file. The rest of the nine zip files consists of datasets for a given split and overlap. For example, the ov3_split1.zip file consists of the audio and metadata folders for the case of maximum three temporally overlapping sound events (ov3) and the first cross-validation split (split1). Within each audio/metadata folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Sound event localization and detection of overlapping sources using convolutional recurrent neural network</a>' work.</p>
TUT Sound Events 2018 - Ambisonic, Reverberant and Synthetic Impulse Response Dataset
<p><strong>Tampere University of Technology (TUT) Sound Events 2018 - Ambisonic, Reverberant and Synthetic Impulse Response Dataset</strong></p> <p>This dataset consists of simulated reverberant first order Ambisonic (FOA) format recordings with stationary point sources each associated with a spatial coordinate. The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), spatial location in azimuth and elevation angles (in degrees), and distance from the microphone (in meters). The sound events are spatially placed within a room using the image source method. The room size chosen was 10x8x4 meter with reverberation time per octave band of [1.0, 0.8, 0.7, 0.6, 0.5, 0.4] s and 125 Hz–4 kHz band center frequencies.</p> <p>The isolated sound events were taken from the <a href="https://archive.org/details/dcase2016_task2_train_dev">DCASE 2016 task 2 dataset.</a> This dataset consists of 11 sound event classes such as Clearing throat, Coughing, Door knock, Door slam, Drawer, Human laughter, Keyboard, Keys (put on a table), Page turning, Phone ringing and Speech. The sound events are randomly placed in a spatial grid with 10-degree resolution in full azimuth and [-60 60) degree elevation angles. Additionally, the sound events are placed at a random distance of at least 1 meter away from the microphone.</p> <p>The license of the dataset can be found in the LICENSE file. The rest of the nine zip files consists of datasets for a given split and overlap. For example, the ov3_split1.zip file consists of the audio and metadata folders for the case of maximum three temporally overlapping sound events (ov3) and the first cross-validation split (split1). Within each audio/metadata folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Sound event localization and detection of overlapping sources using convolutional recurrent neural network</a>' work.</p> <p> </p>
TUT Sound Events 2018 - Ambisonic, Reverberant and Real-life Impulse Response Dataset
<p><strong>Tampere University of Technology (TUT) Sound Events 2018 - Ambisonic, Reverberant and Real-life Impulse Response Dataset</strong></p> <p>This dataset consists of real-life first order Ambisonic (FOA) format recordings with stationary point sources each associated with a spatial coordinate. The dataset was generated by collecting impulse responses (IR) from a real environment using the Eigenmike spherical microphone array. The measurement was done by slowly moving a Genelec G Two loudspeaker continuously playing<br> a maximum length sequence around the array in circular trajectory in one elevation at a time. The playback volume was set to be 30 dB greater than the ambient sound level. The recording was done in a corridor inside the university with classrooms around it during work hours.The IRs were collected at elevations −40 to 40 with 10-degree increments at 1 m from the Eigenmike and at elevations −20 to 20 with 10-degree increments at 2 m. </p> <p>The dataset consists of three sub-datasets with a) maximum one temporally overlapping sound events, b) maximum two temporally overlapping sound events, and c) maximum three temporally overlapping sound events. Each of the sub-datasets has three cross-validation splits, that consists of 240 recordings of about 30 seconds long for training split and 60 recordings of the same length for the testing split. For each recording, the metadata file with the same name consists of the sound event name, the temporal onset and offset time (in seconds), spatial location in azimuth and elevation angles (in degrees), and distance from the microphone (in meters).</p> <p>The isolated sound events were taken from the <a href="https://serv.cusp.nyu.edu/projects/urbansounddataset/urbansound8k.html">urbansound8k dataset</a>. This dataset consists of 10 sound event classes such as air_conditioner, car_horn, children_playing, dog_bark, drilling, enginge_idling, gun_shot, jackhammer, siren, and street_music. We do not consider the air_conditioner and children_playing sound events. Further, we only include the sound event examples marked as foreground in the dataset. We used the splits 1, 8 and 9 provided in the urbansound8k as the three CV splits. These splits were chosen as they had a good number of examples for all the chosen sound event classes after selecting only the foreground examples. During the sound scene synthesis, we randomly chose a sound event example and associated it with a random distance among the collected ones, azimuth and elevation angle. The sound event example was then convolved with the respective IR for the given distance, azimuth and elevation to spatially position it.</p> <p>The metadata.zip folder consists of the license and the metadata for the complete dataset. The rest of the nine zip files consists dataset for given split and overlap. For example, the wav_ov3_split1_30db.zip file consists of training and testing recordings for the case of maximum three temporally overlapping sound events (ov3) for the first cross-validation split (split1). Within each audio folder, the filenames for training split have the 'train' prefix, while the testing split filenames have the 'test' prefix.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Sound event localization and detection of overlapping sources using convolutional recurrent neural network</a>' work.</p> <p><strong>Data collector (s): </strong>Fagerlund, Eemi; Koskimies, Aino</p> <p> </p>
Dataset of measured binaural room impulse responses for use in an position-dynamic auditory augmented reality application
<p>The dataset includes measured binaural room impluse responses (BRIRs) using a head and torso simulator (KEMAR). The measurements are realized for six loudspeaker positions. Five loudspeakers are placed in a standardized five-channel surround setup with the left and right speaker at +/-30° from the center speaker. The left-surround and right-surround speaker are placed at +/-120°. The distance to the midpoint of this setup is 3.5 m. The sixth loudspeaker is placed 1.35 m from the midpoint and 150° to the right side of the setup. Loudspeakers of type Geithain R906 are used.</p> <p>The BRIRs are measured at nine positions within the loudspeaker setup. For each position the artificial head is turned 360° in the horizontal plane with a step size of 5°. Next to the midpoint position of the setup, frontal, backward, and lateral positions are measured. The covered plane size is 4m x 4m. The figure 'scheme.png' gives an overview about the names and setup (included in the download of the dataset).</p> <p>A TV studio at the Technische Universität Ilmenau is used as room for the measurements. The figure 'studio.jpg' (included in the download of the dataset) shows the room in a prior configuration as a TV studio (the left wall as scenery setting is removed). The setup is build up in the left side of the room (gray floor area). The room has a total size of 19.5m x 11.5m x 5.5m. The reverberation time (RT60 from 80 Hz to 18 kHz) is approx. 0.7 s. The C50 and C80 are 15dB and 17dB.</p> <p>The BRIR dataset is used for the synthesis of new BRIRs at diffferent positions in the room using methods described and evaluated in [1] and [2]. Please feel free to use the measured BRIRs for your research and your project! </p> <p>---<br> [1] Brandenburg, K., Cano, E., Klein, F., Köllmer, T., Lukashevich, H., Neidhardt, A., Sloma, U., and Werner, S., “Plausible Augmentation of Auditory Scenes Using Dynamic Binaural Synthesis for Personalized Auditory Realities”, to be published in Proc. of: Conference of the Audio Engineering Society (AES) Audio for Virtual and Augmented Reality, USA, 2018.</p> <p><br> [2] Werner, S., Neidhardt, A., Klein, F., and Brandenburg, K., “Comparison of Different Methods to Create an Interactive Augmented Auditory Reality Scenario Using Sparse Binaural Room Impulse Response Measurements”, in Proc. of DAGA 2018, Garching, Germany, 2018.</p> <p> </p>
TUT Tietotalo Ambisonic Impulse Response
<p><strong>Tampere University of Technology (TUT) Tietotalo Ambisonic Impulse Response</strong></p> <p>This dataset consists of impulse responses (IR) from a real environment using the Eigenmike spherical microphone array. The recordings were done in a fairly large spaced corridor inside the university (Tietotalo building) with classrooms around it. The IR acquisition was done using a maximum length sequence (MLS). The measurement was done by slowly moving a Genelec G Two loudspeaker continuously playing the MLS around the Eigenmike in a circular trajectory. The playback volume was set to be 30 dB greater than the ambient sound level. The IRs were collected at elevations −40 to 40 with 10-degree increments at 1 m from the Eigenmike and at elevations −20 to 20 with 10-degree increments at 2 m. </p> <p>The moving-source IRs were obtained by a freely available tool from CHiME challenge which estimates the time-varying responses in STFT domain by forming a least-squares regression between the known measurement signal and the far-field recording independently at each frequency. The IR for any azimuth within one trajectory can be analyzed by assuming block-wise stationarity of acoustic channel. The CHiME IR estimation tool was applied independently on all 32 channels of the Eigenmike. For the dataset creation, we analyzed the DOA of each time frame using MUSIC and extracted IRs for azimuthal angles at 10° resolution (36 IRs for each elevation).</p> <p>The IR file is in .mat format and can be read both in Matlab and Python. The details of the IR file are as following,</p> <p>Size: (2, 9, 1025, 36, 4, 32) = (distance_wrt_mic, elevation_wrt_mic, FFT, azimuth_wrt_mic, blocks, channels).</p> <p>where,</p> <p>distance_wrt_mic = two distances (1m and 2m)<br> elevation_wrt_mic = 9 elevation angles (-40:10:40) at distance 1m, and 5 elevations angles (-20:10:20) at distance 2m.<br> azimuth_wrt_mic = 36 azimuth angles (-180:10:180) for all distance-elevation combination<br> The IRs were extracted assuming block-wise stationarity (four blocks) for each frequency bin (1025 bins).</p> <p>During synthesis, after convolving the IR with a sound event, the 32 channel audio will have to be transformed to Ambisonic format using the transformation matrix of Eigenmike.</p> <p>This dataset was collected as part of the '<a href="https://github.com/sharathadavanne/seld-net">Sound event localization and detection of overlapping sources using convolutional recurrent neural network</a>' work, more details about this IR dataset can be found in this work.</p> <p><strong>Data collector (s):</strong> Fagerlund, Eemi; Koskimies, Aino</p>
360° Binaural Room Impulse Response (BRIR) Database for 6DOF spatial perception research
<p>by Applied Psychoacoustics Lab, University of Huddersfield</p> <p> </p> <p>Authors: Bogdan Bacila and Hyunkook Lee</p> <p>bogdan.bacila@hud.ac.uk, h.lee@hud.ac.uk</p> <p> </p> <p><strong>Description</strong></p> <p>An open-access database for 360° binaural room impulse responses (BRIR) captured in a reverberant concert hall. Head-rotated BRIRs were acquired with 3.6° angular resolution for each of 13 different receiver positions, using a custom-made head-rotation system that was automated and integrated with the Huddersfield Acoustical Analysis Research Toolbox. The BRIRs are provided in the SOFA format. The library also contains impulse responses captured using a first-order Ambisonic microphone and an omnidirectional microphone. It is expected that the database would be useful for studying the perception of spatial attributes in a six degrees-of-freedom context.</p> <p> </p> <p><strong>Folder Structure</strong></p> <p>The impulse responses are organised into two main folders:</p> <p>* Binaural: Contains the SOFA files and MATLAB files for each position, with a 3.6° angular resolution, recorded with the Neumann KU100 binaural head.</p> <p>* FOA: Contains the First Order Ambisonics audio files in A format and B format for each individual position, recorded with an Sennheiser Ambeo microphone in an end-fire configuration. </p> <p> </p> <p><strong>Naming Convention</strong></p> <p>The files are named after their relative position on the stage and the distance from the stage:</p> <p>* C = Centre</p> <p>* L = Left</p> <p>* LW = Left Wide</p> <p> </p> <p><strong>License</strong></p> <p>This project is licensed under the CC-BY-4.0 License - see the License.txt file for details</p> <p> </p> <p><strong>Publication</strong></p> <p>This database was presented at the Audio Engineering Society 146th International Convention.</p> <p>Download link: http://www.aes.org/e-lib/browse.cfm?elib=20371</p> <p> </p> <p><strong>Referencing</strong></p> <p>If you use the database for your research, please reference it as follows.</p> <p>Bacila, B. I., & Lee, H. (2019). 360° Binaural Room Impulse Response (BRIR) Database for 6DOF Spatial Perception Research. Presented at the Audio Engineering Society Convention 146, Dublin, e-Brief 513</p>
GIR Dataset: A Geometry and real Impulse Response Dataset
<p>The GIR dataset is an open source dataset composed of more than 900k Impulse Responses (IRs). To foster the research in computational acoustics and in particular on the relationship between geometry and acoustic parameters, the measurements include acoustic responses acquired in 2952 positions and 312 surfaces. To maximize consistency, surfaces were 3D printed and the measurement process was automated using two robots. Detailed explanation of the dataset creation methods and possible applications are discussed in Rust <em>et al.</em> 2021 (<a href="https://doi.org/10.1177/1351010X20986901">https://doi.org/10.1177/1351010X20986901</a>) and Xydis <em>et al.</em> 2021 (in prep).</p> <p>The provenance and metadata tracked on Renku is enriched with the additional attributes recommended in the framework <a href="https://arxiv.org/abs/1805.03677">dataset nutrition labels</a> and Schema.org note on <a href="https://schema.org/docs/data-and-datasets.html">Data and Datasets</a>. This enriched metadata is provided in a standard JSON-LD format on Renku: <a href="https://renkulab.io/projects/ddad/gir-dataset/files/blob/dataset.json">dataset.json</a>.</p> <p>Dataset is released under <a href="https://renkulab.io/projects/ddad/gir-dataset/files/blob/LICENSE">the GNU General Public License v3.0</a></p>
Simulated Room Impulse Response for 44.1k Audio
<p>We randomly simulated a collection of Room Impulse Response filters to simulate the 44.1kHz speech room reverberation using an open-source tool (<a href="https://github.com/sunits/rir">https://github.com/sunits/rir</a>_simulator_python). The meters of height, width, and length of the room are sampled randomly in a uniform distribution U(1,12). The placement of the microphone is then randomly selected within the room space. For the placement of the sound source, we first determined the distance between the microphone and sound source, which is randomly sampled in a Gaussian distribution N(\mu,\sigma^2), \mu=2, \sigma=4. If the sampled value is negative or greater than five meters, we will sample the distance again until it meets the requirement. After determined the distance between the microphone and sound source, the placement of the sound source is randomly selected on the sphere centered at the microphone. The RT60 value we choose comes from the uniform distribution U(0.05,1.0). For the pickup pattern of the microphone, we randomly choose from types omnidirectional and cardioid. </p> <p>If you found this dataset helpful, please consider citing: </p> <blockquote> <pre>@article{liu2021voicefixer, title={VoiceFixer: Toward General Speech Restoration with Neural Vocoder}, author={Liu, Haohe and Kong, Qiuqiang and Tian, Qiao and Zhao, Yan and Wang, DeLiang and Huang, Chuanzeng and Wang, Yuxuan}, journal={arXiv preprint arXiv:2109.13731}, year={2021} }</pre> </blockquote>
A dataset of measured spatial room impulse responses for the transition between coupled rooms
<p>For a detailed description of the measurement and analysis methods, please see:</p> <p>McKenzie, T., Schlecht, S. J., and Pulkki, V. (2021). Acoustic Analysis and Dataset of Transitions between Coupled Rooms. <em>IEEE International Conference on Acoustics, Speech and Signal Processing.</em></p> <p> </p> <p>This dataset contains measured spatial room impulse responses for the transition between coupled rooms. Four coupled room pairs are included:</p> <ul> <li>Meeting Room to Hallway</li> <li>Office to Anechoic Chamber</li> <li>Office to Kitchen</li> <li>Office to Stairwell</li> </ul> <p>All were recorded at the Aalto University campus using a Genelec 8331A coaxial loudspeaker and an mh acoustics Eigenmike (32 capsule spherical microphone array for fourth order spherical harmonic capture). Each transition features 101 measurements in 5cm intervals from 2.5m inside the first room to 2.5m inside the second room. Transitions are repeated four times corresponding to four different source positions: two inside each room, one which has no continuous line-of-sight with the microphone when in the opposing room, and one which retains a direct continuous line-of-sight with the microphone for all measurement positions. </p> <p>The spatial room impulse responses are downloadable in either Spatially Oriented Format for Acoustics (SOFA) and Wav formats. Supplementary data includes amplitude plots, direct-to-reverberant graphs, estimated direction-of-arrival plots and scaled illustrations of room geometries and source positions. </p> <p>Changelog: </p> <p>V 1.0 - Initial version<br> V 1.1 - Added plots only download option<br> V 1.2 - Improved time alignment of impulse responses and changed normalisation to a single value relative to the maximum of the entire dataset<br> V 1.3 - SOFA files updated to latest Matlab API (1.1.3), 'SingleRoomDRIR' convention, with SourcePosition data corrected. The SOFA files for each transition are also now downloadable separately, in case the entire dataset is not required. <br> V 1.4 - SRIRs have been denoised using the technique described in https://www.aes.org/e-lib/browse.cfm?elib=21800 and available at https://github.com/chris-hld/Directional-Multi-Slope-Room-Impulse-Response-Denoising. This is particularly noticeable for measurements with a low SNR, such as where there is a large distance between source and receiver and occluded direct path. Note that the wav files have not been included in this release - see previous releases if wav files (not denoised) are required. SOFA files are renamed and available to download separately.</p>
Comparison of 2D and 3D Multichannel Audio Rendering Methods for Hearing Research Applications using Technical and Perceptual Measures - Impulse Responses and Scene Recordings
<p>This database contains recordings of impulse responses (IRs.zip) and virtual acoustic scenes (scenes.zip), with different 2D and 3D multichannel loudspeaker rendering methods. This upload contains conplementary data to [1].</p> <p>The virtual acoustic scenes are a 'concert' of an orchestra with approximately 60 primary sound sources [2] in a reverberant room, a 'speech' scene with a single talker in the same room, and a 'street' scene with static and moving sound sources.</p> <p>Recordings were performed with a G.R.A.S. 45BB Head and Torso Simulator (Kemar) placed in the center of the loudspeaker array in the lab, with ears at 1.60 m height. Recordings include the left and right ear channel. The simulator was equipped with large anthropometric pinnae of type KB5001. All recordings are provided for each rendering method that was applied in the study [1].</p> <p>Impulse responses were recorded using sine sweeps [3], sampling frequency was 44100 Hz. The impulse responses were truncated to 1.2 s. The scenes were recorded with a sampling frequency 44100 Hz.</p> <p> </p> <p>Files are named with the following convention:</p> <p>filetype_scene_renderingmethod_source.[mat/wav]</p> <p> </p> <p>References:</p> <p>[1] M. Gerken, V. Hohmann, G. Grimm, "Comparison of 2D and 3D Multichannel Audio Rendering Methods for Hearing Research Applications using Technical and Perceptual Measures," Acta Austica 2024, in press.</p> <p>[2] C. Böhm, D. Ackermann, and S. Weinzierl, "A Multi-channel Anechoic Orchestra Recording of Beethoven's Symphony No. 8 op. 93," Journal of the Audio Engineering Society, vol. 68, no. 12, pp. 977–984, Jan. 2021, doi: 10.17743/jaes.2020.0056.</p> <p>[3] A. Farina, "Simultaneous Measurement of Impulse Response and Distortion with a Swept-Sine Technique," in Audio Engineering Society Convention 108, Feb. 2000. [Online]. Available: http://www.aes.org/e-lib/browse.cfm?elib=10211</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.