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99 results for “binaural”
Binaural room impulse responses recorded with KEMAR in a small meeting room
<p>The binaural room impulse responses (BRIRs) were measured in the small meeting room Spirit at the<br> 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]</p> <p>[1] Wierstorf, H., Geier, M., Raake, A., Spors, S. (2011) “A Free Database of Head-Related Impulse Response Measurements in the Horizontal Plane with Multiple Distances,” 130th AES Convention, eBrief 6</p>
Dataset for: A hemispheric two-channel code accounts for binaural unmasking in humans
<p><strong>Dataset for the paper: A hemispheric two-channel code accounts for binaural unmasking in humans.</strong></p> <p>The model code to generate this data has been published here: <a href="https://doi.org/10.5281/zenodo.5643429">https://doi.org/10.5281/zenodo.5643429</a></p>
Binaural Impulse Response Dataset: Square Plate in Anechoic Chamber
<p><span>The dataset at hand contains impulse responses that have been measured along two discretized trajectories in the vicinity of a 25 mm thick 1 m x 1 m medium density fiberboard plate. Such data can serve as reference for the modelling of acoustic edge diffraction. This dataset was used in the context of research on binaural perception of diffracted sound in a publication that is in press at the Journal of the Acoustical Society of America</span></p>
Monaural and binaural sound localization cues in crocodilians
<p>This dataset is composed by all the recorded microphonic signals necessary for the computation of external sound localization cues: HRTFs (Head-Related Transfer Functions), Interaural Level Differences (ILD) and Interaural Time Differences (ITD) on awake crocodilians (<em>Crocodylus niloticus</em> and <em>Caiman latirostris</em>) and skulls(<em>Crocodylus niloticus</em>).</p> <p>The Matlab scripts necessary to compute and display HRTF, ILD and ITD are included as well as instructions in txt and pdf files.</p>
Dataset for: Effects of acute ischemic stroke on binaural perception, Dietze et al., Frontiers in Neurosciences, 2022
<p>This dataset contains the MNI-registered lesion masks of patients with strokes at different locations and the psychoacoustic results (tone in noise detection and lateralization) of stroke and control groups.<br> The dataset is described in Dietze A, Sörös P, Bröer M, Methner A, Pöntynen H, Sundermann B, Witt K and Dietz M (2022) Effects of acute ischemic stroke on binaural perception. Front. Neurosci. 16:1022354. doi: 10.3389/fnins.2022.1022354</p>
Greek Theatre of Tyndari: auralized binaural tracks
<ul> <li><em>IPHIGENIA-Singer_rotation_Receiver_1-Source_1.wav</em> (The singer starts singing by pointing at receiver R1, makes two turns around herself in a clockwise direction and finishes by pointing at receiver R1 again)</li> <li><em>IPHIGENIA-Singer_rotation_Receiver_6-Source_1.wav</em> (The singer starts singing by pointing at receiver R6, makes two turns around herself in a clockwise direction and finishes by pointing at receiver R6 again)</li> </ul>
Dynamic Binaural Processing (sBTRF) data
<p>This dataset is associated with a publication exploring processing of dynamic binaural cues. </p>
Associated dataset for "Instrumental Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals"
<p>The conducted instrumental evaluation utilizes the Real-Time Spherical Microphone Renderer (<a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR">ReTiSAR</a>) for binaural reproduction in Python. The at that time employed code state should be used in order to exactly reproduce the rendering results in this data set. The frozen code state for this data set is available at:<br> <a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.FA">https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.FA</a></p> <p>Download the rendering pipeline and follow the setup instructions! Use the here included Conda environment file when setting up the Python environment. In this way, you will obtain exactly the same Python setup as utilized in the instrumental evaluation in the publication:</p> <pre><code class="language-bash">conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code class="language-bash">source activate ReTiSAR_FA_freeze</code></pre> <p>Directory "SMA sampling grids":</p> <ul> <li>Visualization of spatial arrangement (like Figure 4) for all investigated spherical microphone array rendering configurations (Table 1)</li> </ul> <p>Shell script "record_snr.sh":</p> <ul> <li>Record the input and output signals of the rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all configurations at multiple head orientations</li> <li>All captured signals are contained in the "SNR" directory</li> </ul> <p>Matlab script "calculate_snr.m":</p> <ul> <li>Visualize the raw captured input and output signals (like Figure 1 for all configurations)</li> <li>Visualize the resulting signal-to-noise ratio (like Figure 2 for all configurations)</li> <li>Visualize the comparison of the resulting signal-to-noise ratio of all configurations (Figure 3, also for the resulting SNR from signals with A-weighting)</li> <li>All generated plots are contained in the "SNR" directory</li> </ul> <p>Shell script "record_noise.sh":</p> <ul> <li>Record the calibration and noise signals of the mh acoustic Eigenmike 32 spherical microphone array in the anechoic chamber at Chalmers University of Technology (Appendix)</li> <li>All captured signals are contained in the "EM32 measurements" directory</li> <li>Pictures of the measurement setup are contained in the "Pictures" subdirectory</li> </ul> <p>Matlab script "calculate_EM32_noise_levels.m":</p> <ul> <li>Determine the resulting target signal sensitivity and equivalent input noise levels for the investigated pre-amplification gains (Table 2)</li> <li>Visualize the statistical distribution of the individual raw and weighted SMA channels (like Figure 6 for all configurations)</li> <li>Visualize the spatial distribution of the individual raw and weighted SMA channels for all configurations</li> <li>Visualize the smoothed and averaged magnitude spectra of the individual raw and weighted SMA channels (like Figure 5 for all configurations)</li> </ul>
Binaural Impulse Responses
<p>Impulse responses were recorded with a single 12s long sweep with a B&K HATS without ear canals. The room is an anechoic chamber with 64 loudspeaker arranged in a full sphere. The locations of the loudspeakers are shown in the files loudspeakerLocations (azimuth, elevation in radians). The distance of the loudspeaker to the centre of the artificial head is 2.4m. The loudspeaker were equalized beforehand.</p> <p>The short IRs were cutted to have a length of 512 samples and the long version to 8192 samples. The sampling frequency is 48 kHz.</p>
Ambisonic Stimuli Files and Binaural Renders
<p>This repository hosts the audio files used to render the layer-based stimuli for the experiment described in 'A Study on Loudspeaker SPL Decays for Envelopment and Engulfment across an Extended Audience' (2024 AES International Conference on Acoustics and Sound Reinforcement). </p><p>Binaural renders of the 7th-order Ambisonic stimuli are included for headphone listening.</p>
Binaural recordings from REEM-C Humanoid
<p>Binaural audio recordings from REEM-C head, along with corresponding ground truth for DOA and speech windows</p>
ICAD 2024 MMS Binaural Audification
<p>ICAD 2024 Auralization of Magnetic Multiscale Satellite Data: Toward Integrated Audification in Space Science</p> <p>Authors: Kristina Collins, Robert L. Alexander, Jaye Verniero, Robert M. Candey</p> <p>Video Production: Robert L. Alexander, Kristina Collins </p> <p>MMS Visualization: NASA's Scientific</p> <p>Visualization Studio Visualizer: Tom Bridgman (Global Science and Technology, Inc.) </p> <p>Scientist: Tai Phan (University of California at Berkeley)</p> <p>Producer: Joy Ng (USRA)</p> <p>Writer: Mara Johnson-Groh (Wyle Information Systems)</p>
Associated dataset for "Effects of Additive Noise in Binaural Rendering of Spherical Microphone Array Signals"
<p>The instrumental evaluation utilized the Real-Time Spherical Microphone Renderer (<a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR">ReTiSAR</a>) for binaural reproduction in Python. The employed code state at that time should be used to reproduce the rendering results in this data set exactly. The frozen code state for this data set is available at:<br><a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2021.TASLP">https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2021.TASLP</a></p> <p>Download the rendering pipeline and follow the setup instructions! Use the Conda environment file included here when setting up the Python environment. In this way, you will obtain the exact Python setup as utilized in the instrumental evaluation in the publication:</p> <pre><code>conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code>source activate ReTiSAR_TASLP_freeze</code></pre> <p>Matlab script "generate_norm_levels.m":</p> <ul> <li>The level contributions used in the publication are contained in "record_CLL_levels.sh", therefore this needs to be executed only in case other level distributions should be generated</li> <li>Generate the string for a ReTiSAR configuration (as used in "record_CLL_levels.sh") to emulate normally contributing EM32 self-noise based on Forum Acusticum publication data</li> <li>Generate the string for aReTiSAR configuration (as used in "record_CLL_levels.sh") to emulate normally contributing GL162 self-noise based on Gaussian normal distribution</li> </ul> <p>Matlab script "prepare_MagLS_HRIRs.m":</p> <ul> <li>Apply Magnitude Least Squares pre-processing to HRIRs (as used in "record_CLL_levels.sh")</li> </ul> <p>Shell script "record_CLL_levels.sh":</p> <ul> <li>Record the output ear signals of the rendering pipeline at multiple head orientations for all investigated configurations (according to Table 1)</li> <li>All captured signals are contained in the respective configuration directory, e.g. "rec_25ch_Fliege_sh4" to "rec_338ch_Gauss_sh12"</li> </ul> <p>Matlab script "calculate_CLL_levels.m":</p> <ul> <li>Read the individual rendering pipeline output recordings for arbitrary configurations</li> <li>Visualize the raw captured signals per configuration</li> <li>Visualize the RMS signal level variations over all head orientations per configuration</li> <li>Visualize the Interaural Level Difference variations over all head orientations per configuration</li> <li>Visualize the Composite Loudness Level variations over all head orientations per configuration (like Figure 2)</li> <li>Gather the above determined RMS, ILD, CLL, etc. metrics in a Matlab dataset per configuration (will be utilized in "plot_gathered_CLL_levels.m")</li> <li>All generated plots and Matlab datasets are contained in the respective configuration directory, e.g. "rec_25ch_Fliege_sh4" to "rec_338ch_Gauss_sh12"</li> </ul> <p>Matlab script "plot_gathered_CLL_levels.m":</p> <ul> <li>Visualize the resulting Composite Loudness Level gradient detections over all head orientations for arbitrary combinations of configurations (like Figure 3 to Figure 14)</li> <li>All generated plots are contained in the "CLL_results" directory</li> </ul>
Ambisonic-Binaural
<p>A dataset collected for ambisonic-based binaural rendering. The recordings are collected at ByteDance in Nov. 2021. We recorded 31 minutes and 18 minutes of audios for training and testing respectively. These audios are paired ambisonics and binaurals.</p>
BINCI 360º demo video with binaural audio experimental
<p>BINCI - Binaural Tools for the Creative Industries</p> <p>There are multiple use cases as well as interpretations about immersive audio. As an aid for understanding BINCI approach of immersive audio, we created this first 360 demo video with Matroska Spatial Workstation 8 Channel. You can download this video and watch and hear with Google Cardboard or even better, with Samsung Gear. </p>
Experiment resources for "Quality of Binaural Rendering From Baffled Microphone Arrays Evaluated Without an Explicit Reference"
<div>This data set contains the following resources to reproduce the listening experiment and statistical analysis of the referenced manuscript:</div> <div> <ul> <li>The <em>binaural room impulse responses</em> (<strong>BRIR</strong>s) of all listening conditions presented in the perceptual experiment.</li> <li>The tools to create the listening test infrastructure, including <em>Pure Data</em> (<strong>Pd</strong>) patches and configuration files for the <em>SoundScape Renderer</em> (<strong>SSR</strong>) and <em>graphical user interface</em> (<strong>GUI</strong>).</li> <li>The raw response data as gathered from the experiment subjects.</li> <li>The R and Stan scripts to perform the statistical analysis and generate the resulting plots and tables.</li> </ul> </div> <div> <p> </p> <p>The archive contains the following components described below.</p> <p>Directory "dependencies/":</p> <ul> <li>Matlab, R, Stan, and Pd functions that are utilized in the code and experimental setup</li> <li>Additional dependencies of available open-source projects may be required for certain code functions. If so, the source and setup process for the necessary dependencies are documented in the file header.</li> </ul> </div> <div> <p>Directory "plots/4_Equalization/":</p> <ul> <li>Plots of all available headphone equalization filters as generated by the following Matlab scripts.</li> </ul> <p>Directory "plots/8_User_study/":</p> <ul> <li>Plots of the raw and analyzed experimental results as generated by the following Matlab and R scripts.</li> </ul> </div> <div> <p>Directory "resources/BRIR_auralization/":</p> <ul> <li>Audio files with static binaural auralizations of all listening conditions presented in the perceptual experiment as generated by the following Matlab scripts.</li> <li>The files in "Kemar_HRTF_sofa_N44_adjusted" do not include a headphone equalization.</li> <li>The files in "Kemar_HRTF_sofa_N44_adjusted+Sennheiser_HD650_lin" include the equalization for the <em>Sennheiser HD650</em> headphones employed in the listening experiment. The files are identical to the annotated <a href="http://www.ta.chalmers.se/research/audio-technology-group/audio-examples/jaes-2024a/" target="_blank" rel="noopener">listening examples</a> published for the manuscript.</li> </ul> <p>Directory "resources/BRIR_rendered/":</p> <ul> <li>BRIRs and rendering parameters of all listening conditions presented in the perceptual experiment as generated from the associated <a href="../doi/10.5281/zenodo.8206570" target="_blank" rel="noopener">data set</a> and <a href="https://github.com/HaHeho/baffled-arrays-to-binaural/releases/tag/v2024.JAES" target="_blank" rel="noopener">rendering code</a>.</li> <li>Scene configuration files for the SSR with all listening conditions presented in the perceptual experiment as generated from the following Matlab scripts.</li> </ul> <p>Directory "resources/HPCF_KEMAR/":</p> <ul> <li>Impulse responses of equalization filters for various headphones on the G.R.A.S KEMAR acoustic dummy head as measured for this experiment.</li> </ul> <p>Directory "resources/User_study/":</p> <ul> <li>Various resources for the listening experiment.</li> <li>The files in "Exp1_analysis" include intermediate and final statistical analysis results as generated from the following R scripts.</li> <li>"Exp1_config.json" contains the configuration of the study GUI with conditions presented in the listening experiment.</li> <li>"Exp1_Introduction.pdf" contains the instructions presented to the subjects at the start of the listening experiment.</li> <li>"Exp1_Part2_data_strings.xls" contains all subjects' raw perceptual response data gathered from the listening experiment.</li> <li>"Questionnaire.pdf" contains the questionnaire given to the subjects at the end of the listening experiment.</li> </ul> <p>Matlab script "x4_Gather_Headphone_Compensations.m":</p> <ul> <li>Generate plots of measured headphone compensation filters. Furthermore, the generated minimum phase filters and filters to yield a linear phase response from the headphones are extracted as separate WAV files.</li> </ul> </div> <div> <p>Matlab script "x6_Gather_SSR_Configurations.m":</p> <ul> <li>Collect several specified pre-rendered binaural room impulse response sets into an ASD file. The SSR can load this scene to present all gathered configurations in direct comparison with head tracking.</li> </ul> </div> <div> <p>Readme file "x6a_Normalize_SSR_Loudnesses.txt":</p> </div> <div> <div> <div> <ul> <li>Ideally, the rendering script would implement a measure to provide a reliable estimation of the binaural loudness of the rendered configuration. This could be used to normalize all stimuli levels. However, such a measure is currently not available or implemented.</li> <li>Therefore, tuning the stimuli loudness for the user study was performed beforehand by ear. The adjusted playback levels are set in a modified SSR configuration file for the listening experiment.</li> </ul> </div> <div> <p>Matlab script "x6_Gather_SSR_Configurations.m":</p> <ul> <li>Perform convolution of (rendered) binaural room impulse responses with a source audio signal. This is done for a specified selection of static head orientations and a continuous rotation over all horizontal head orientations.</li> <li>The resulting auralizations are published as <a href="http://www.ta.chalmers.se/research/audio-technology-group/audio-examples/jaes-2024a/">supplementary materials</a> to the manuscript.</li> </ul> <p>Shell script "x7_Start_Study_GUI.sh":</p> <ul> <li>Initialize all required components to perform the perceptual user study, including: <ul> <li>SSR to perform the real-time rendering of the BRIRs with head tracking</li> <li>SSR to extract head-tracking data (in case a Polhemus tracker is used)</li> <li>Pd to extract head-tracking data (in case a Supperware tracker is used)</li> <li>Pd to perform real-time convolution to apply headphone compensation</li> <li>Pd to receive OSC messages from the study GUI</li> <li>Pd to trigger audio file playback from received OSC messages</li> <li>Pd to translate OSC messages into FUDI messages for the SSR</li> <li>The GUI to be used by the participants and implement the study procedure</li> </ul> </li> <li>Some static configuration variables can be adjusted, whereas other parameters are chosen during script execution.</li> </ul> <p>Matlab script "x8_Gather_Study_Data.m":</p> <ul> <li>Transform the raw result data from the questionnaire (*.xls) and the study GUI (*.json) into a compact format (*.xls) that can be loaded to plot the raw data and imported by software for the subsequent statistical analysis.</li> <li>Note that this contains the responses from all subjects, whereas responses from the investigators must be excluded from the statistical analysis (which is implemented in the analysis scripts).</li> </ul> <p>Matlab script "x8a_Plot_Study_Data.m":</p> <ul> <li>Generate a set of violin plots to visualize the initial distribution of the raw perceptual data. The data is split by specified attributes and plotted separately for visual inspection.</li> <li>The data may also be transformed into ranks for a first distribution inspection. Note that implementing the ranking method, notably how ties are resolved, may differ from the technique employed in the statistical analysis.</li> <li>Note that this contains the responses from all subjects, whereas responses from the investigators must be excluded from the statistical analysis (which is implemented in the analysis scripts).</li> </ul> <p>R script "x8b_Analyze_Exp1_Data.R":</p> <ul> <li>Perform the statistical analysis by transforming the observed subject ratings into a predicted distribution of ranks using a hierarchical generalized linear regression model.</li> <li>Executing the statistical model may take some time due to the Bayesian framework employing Markov-chain Monte Carlo simulations.</li> <li>Data is exported at various intermediate steps to be loaded and visualized by the following R script.</li> </ul> <p>R markdown script "x8c_Plot_Exp1_Results.Rmd":</p> <ul> <li>Generate various plots and data tables of the observed data and the predicted results to visualize the distribution and influence of different analysis parameters.</li> <li>Some of the resulting plots were used in the manuscript.</li> <li>"x8c_Plot_Exp1_Results.html" conveniently summarizes all plots and data tables generated by "knitting" the R markdown script.</li> </ul> </div> </div> </div>
Binaural Impulse Responses with and without HTC Vive HMD
<p>Impulse responses (IRs) were recorded with a single 20s long sweep with a B&K 4128 HATS with ear canals. The room is an anechoic chamber with 64 loudspeakers (KEF LS50) arranged in a full sphere. The locations of the loudspeakers are shown in the files loudspeakerLocations (azimuth, elevation in radians). The distance of the loudspeaker to the centre of the artificial head is 2.4m. The loudspeaker were equalized beforehand.</p> <p>The files including the term "HMD" (head mounted display) or "vive" were measured with the B&K 4128 HATS wearing the HTC Vive HMD.</p> <p>The sampling frequency is 48 kHz.</p> <p>The IRs were truncated to allow at least 256 samples (5.3ms) after the main peak.</p>
TAU-SEBin Binaural Sound Events 2021
<p><strong>TAU-SEBin Binaural Sound Events 2021 </strong>is a dataset of synthetic binaural audio recordings, which consist of sound events spaced in simulated shoebox rooms. The data is suitable for experiments with several acoustic scene analysis tasks such as sound source localization, sound distance estimation or sound event detection.</p> <p> </p> <p>Data is created using isolated sound events derived from several datasets: NIGENS [1], DESED [2] and TUT Rare Sound Events 2017 [3], containing 18 total sound classes, namely: alarm, baby, blender, cat, crash, dishes, dog, engine, fire, footsteps, glassbreak, gunshot, knock, phone, piano, scream, speech, water. The data is split into two subsets, one of which (bin_prox_dir) contains up to two overlapping sound events, whereas the other one consists of single sources only (bin_prox_dir_one). Each subset contains 400 audio files, divided into 4 equal splits for fold-wise cross-validation.</p> <p> </p> <p>The metadata provides the following information:</p> <p><strong>sound_event_recording</strong> - sound event class</p> <p><strong>start_time, end_time - </strong>onset and offset times of the sound events (in seconds)</p> <p><strong>azi, ele - </strong>the azimuth and elevation angle of the sound source (in degrees)</p> <p><strong>dist</strong> - sound source to receiver distance (in metres)</p> <p> </p> <p> </p> <p>References:</p> <p>[1] I. Trowitzsch, J. Taghia, Y. Kashef, and K. Obermayer, NIGENS general sound events database. Zenodo, 2019.<br> [2] N. Turpault, R. Serizel, A. Parag Shah, and J. Salamon, “Sound event detection in domestic environments with weakly labeled data and soundscape synthesis,” in Workshop on Detection and Classification of Acoustic Scenes and Events, 2019.<br> [3] A. Mesaros, T. Heittola, A. Diment, B. Elizalde, A. Shah, E. Vincent, B. Raj, and T. Virtanen, “DCASE 2017 challenge setup: Tasks, datasets and baseline system,” in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), 2017, pp. 85–92.</p>
Binaural detection thresholds and audio quality of speech and music signals in complex acoustic environments
<p>Every-day acoustical environments are often complex, typically comprising one attended target sound in the presence of interfering sounds (e.g., disturbing conversations) and reverberation. Here we assessed binaural detection thresholds and (supra-threshold) binaural audio quality ratings of four distortions types: spectral ripples, non-linear saturation, intensity and spatial modifications applied to speech, guitar, and noise targets in such complex acoustic environments (CAEs). The target and (up to) two masker sounds were either co-located as if contained in a common audio stream, or were spatially separated as if originating from different sound sources. The amount of reverberation was systematically varied. Masker and reverberation had a significant effect on the distortion-detection thresholds of speech signals. Quality ratings were affected by reverberation, whereas the effect of maskers depended on the distortion. The results suggest that detection thresholds and quality ratings for distorted speech in anechoic conditions are also valid for rooms with mild reverberation, but not for moderate reverberation. Furthermore, for spectral ripples, a significant relationship between the listeners’ individual detection thresholds and quality ratings was found. The current results provide baseline data for detection thresholds and audio quality ratings of different distortions of a target sound in CAEs, supporting the future development of binaural auditory models.</p>
BRUDEX Database: Binaural Room Impulse Responses with Uniformly Distributed External Microphones
<p>There is an emerging need for comparable data for multi-microphone processing, particularly in acoustic sensor networks. However, commonly available databases are often limited in the spatial diversity of the microphones or only allow for particular signal processing tasks. In this paper, we present a database of acoustic impulse responses and recordings for a binaural hearing aid setup, 36 spatially distributed microphones spanning a uniform grid of (5x5) m^2 and 12 source positions. This database can be used for a variety of signal processing tasks, such as (multi-microphone) noise reduction, source localization, and dereverberation, as the measurements were performed using the same setup for three different reverberation conditions (T_60≈{310, 510, 1300} ms). The usability of the database is demonstrated for a noise reduction task using a minimum variance distortionless response beamformer based on relative transfer functions, exploiting the availability of spatially distributed microphones.</p> <p><br>An example how to load a impulse responses corresponding to the 'low' reverberation condition for the speaker located at 60 deg using MATLAB:<br> dataStruct = loadRIR('low',60,1,<basePATH>);%<basePATH>: path where database is located on local machine<br>For further MATLAB examples, please consider "wrapper_loadDataFromDB.m" in the "matlabScripts.zip" archive file.</p> <p>An example how to load a impulse responses corresponding to the 'low' reverberation condition for the speaker located at 60 deg using Python:<br> dataloader = wrapper.BRUDEXDataloader()# <basePATH> is implicitly set to that path, where the file "wrapper.py" is located on local machine<br> dataStruct = dataloader.load_rir(reverberation_condition='low', direction_of_arrival = 60, ha_av = 1,e_mic_run = None)<br>For further Python examples, please consider "main.py" in the "pythonScripts.zip" archive file.</p> <p>Caution: We noticed some problems with the download of the databse when using the command line (e.g., via the zenodo_get, wget, or curl commands). These problems don't seem to appear when downloading the files with the "Download" buttons on the website instead.</p> <p>Caution 2: When processing microphone signals, which are recorded with microphones that are placed *behind* loudspeakers, one can expect direct-path problems.</p> <p>Caution 3: For the recordings of the noise signals, four loudspeakers were placed at about 170 cm from (and facing) the corners of the room. That is why the noise is approximately spatially diffuse only in the vicinity of the center of the room and rather spatially coherent in the corners of the room.</p> <p>Caution 4: Oppposed to Zenodos information, the database does not contain 3 TB of data but about 200 GB.</p> <p> </p> <p>Reference:</p> <p>D. Fejgin, W. Middelberg, and S. Doclo,<br>“BRUDEX database: Binaural room impulse responses with uniformly distributed external microphones,”<br>in Proc. ITG Conference on Speech Communication, Aachen, Germany, Sep. 2023, pp. 1–5.</p> <p>@InProceedings{Fejgin2023,<br> author = {D. {Fejgin} and W. {Middelberg} and S. {Doclo}},<br> booktitle = {Proc. ITG Conference on Speech Communication},<br> title = {{BRUDEX} Database: Binaural Room Impulse Responses with Uniformly Distributed External Microphones},<br> pages = {1-5},<br> month = {Sep.},<br> year = {2023},<br> address = {Aachen, Germany}<br>}</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.