Find research datasets worth reusing

opencc-by-4.0Aug 2024View details →

Supplemental Figures for: "The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves"

Additional figures for the paper The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves.  <h2> </h2> <h2>Interactive Figure Data</h2> Data files used to create the intreactive version of Figure 5 in the publication. There is a version of each file for each line species in the plot (i.e., Hα, Hβ, and MgII). clouds_{line_name}.csv: A CSV file containing the cloud positions, line-of-sight velocities, and weights. The columns of the file are x [light-day], y [light-day], z [light-day], velocity [km/s], and weight. transfer_function_velocity_{line_name}.csv: A CSV file containing x-axis of the transfer function panels, the rest-frame velocity. transfer_function_tau_{line_name}.csv: A CSV file containing the y-axis of the transfer function panels, the rest-frame time delay τ in days. transfer_function_{line_name}.csv: A CSV file containing the transfer function Ψ.   <h2>Model-Related Figures</h2> fitplot_low.pdf: Same as Figure 4 in the publication, but for the low state. fitplot_high.pdf: Same as Figure 4 in the publication, but for the high state. geoplot_low.pdf: Same as Figure 5 in the publication, but for the low state. geoplot_high.pdf: Same as Figure 5 in the publication, but for the high state. lagplot_low.pdf: Same as Figure 6 in the publication, but for the low state. lagplot_high.pdf: Same as Figure 6 in the publication, but for the high state.    <h2>Spectral Reduction Method Comparison</h2> spec_decomp_pyqsofit.pdf: A figure showing the spectral decomposition performed in PyQSOFit for the processed line profiles for Hβ, Hα, and MgII for an example epoch. The total spectrum is shown in black, and each of the decomposed elements are shown, color-coded using the legend above the three panels. input_method_comp.pdf: A figure showing the processed multi-epoch line profiles for each spectral reduction method (PyQSOFit and PrepSpec). Each column corresponds to a given line (labeled above), and each row corresponds to a given spectral reduction method (labeled on the right). Note that the scales for each panel are different.   <h2>Published Value Comparison</h2> pubval_table.pdf: A table comparing the values obtained for certain physically relevant parameters obtained from our BRAINS modeling to those obtained in Shen et al. (2024).    <h2>Joint Posterior Analysis</h2> joint_line_posterior_table.pdf: A table containing the median values (and their uncertainties) extracted from the joint posteriors for a few key model parameters. These joint posteriors are produced for a given state, across all line species.    <h2>Virial Factor Analysis</h2> fcomp.pdf: A comparison of the virial factor values obtained by using the line dispersion (σ) and FWHM of each of the lines in each of the states. fcorr_table.pdf: A table showing the correlations between the virial factor and model parameters (i.e., the slopes obtained using <a href="https://github.com/jmeyers314/linmix">LinMix</a> assuming a linear relationship, and the correlation coefficients). Values are given for virial factors obtained using both the line dispersion (σ) and FWHM.

opencc-by-4.0Feb 2024View details →

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-4.0Jul 2021View details →

Dataset used in "Ocean floor imaging with Distributed Acoustic Sensing and water phases reverberations" by Spica et al. in Geophysical Research Letters

earthquake #1 earthquake #2

opencc-by-4.0Feb 2023View details →

Reverberant Magnetic Resonance Elastographic Using a single Mechanical Driver

Reverberant elastography provides fast and robust estimates of shear modulus. However, reverberant elastography uses multiple mechanical drivers, hampering clinical utility.  In this work, we hypothesize that a single mechanical driver can generate reverberant shear fields in constrained organs such as the brain. To corroborate this hypothesis, we imaged the brain of a healthy volunteer; and two constrained phantoms containing spherical inclusions with diameters ranging from 4-18 mm. As a secondary goal, we assessed the feasibility of recovering shear modulus from a single component of the reverberant wave field. Viable reverberant and subzone elastograms were produced only when obtained at 50 and 100 Hz in phantoms. Different levels of reverberance were exhibited in different displacement components (70-82% for phantoms and 87-93% for the clinical case); however, wavefields obtained when imaging at 50 Hz and 100 Hz were not significantly different (p>0.05). Errors incurred in reverberant elastograms varied from 5% to 65% when imaging at 50 Hz and 2% to 55% when imaging at 100 Hz. Errors incurred in subzone elastograms ranged from 4% to 18% at 50 Hz and 5% and 50% at 100 Hz. The contrast-to-noise ratio of reverberant elastograms ranged from 20 dB to 44 dB compared to 25 dB to 31 dB in subzone elastograms. The accuracy of the elastograms acquired from the phantom containing internal shear wave reflectors did not differ noticeably. The global brain stiffness estimated from reverberant and subzone elastograms was 2.36 ± 0.95 kPa and 2.5 ± 1.1 kPa, respectively, when imaging at 50 Hz, and 2.56 ± 0.828 kPa and 2.89 ± 1.3 kPa respectively, when imaging at 70 Hz. The phantom study revealed that performance varied depending on the component of displacement used to compute reverberant elastograms; however, the clinical study demonstrated similar performance of reverberant and subzone elastograms

opencc-by-4.0Sep 2016View details →

Measured scattering parameters for the coupling of stochastic electromagnetic fields to transmission line networks of single-wire lines above a ground plane in a reverberation chamber

This data set contains the measuremed scattering parameters between two antennas and a transmission line network under test in a reverberation chamber. The purpose of this measurement was an experimental validation of a numerical simulation model for the stochastic field coupling to a transmission line network. For the experiment, an exemplary network consisting of three single-wire lines above a ground plane was created. Different configurations of the network were tested and the average squared magnitude of the coupled voltage at the terminals of the network was analyzed and discussed.

opencc-by-4.0Sep 2024View details →

Relative Transfer Matrix for Low SNR Speech Separation from Noisy Sources in Reverberant Rooms

This folder contains the supplementary audio files for the paper "Relative Transfer Matrix for Low SNR Speech Separation from Noisy Sources in Reverberant Rooms" submitted to The Journal of the Acoustical Society of America.

opencc-by-4.0Aug 2024View details →

Multiple Speaker Separation from Noisy Sources in Reverberant Rooms using Relative Transfer Matrix

This folder is the supplementary audio files for the paper "Multiple Speaker Separation from Noisy Sources in Reverberant Rooms using Relative Transfer Matrix" submitted to the European Signal Processing Conference (EUSIPCO).

dryad36/100

Cortical adaptation to sound reverberation

In almost every natural environment, sounds are reflected by nearby objects, producing many delayed and distorted copies of the original sound, known as reverberation. Our brains usually cope well with reverberation, allowing us to recognize sound sources regardless of their environments. In contrast, reverberation can cause severe difficulties for speech recognition algorithms and hearing-impaired people. The present study examines how the auditory system copes with reverberation. We trained a linear model to recover a rich set of natural, anechoic sounds from their simulated reverberant counterparts. The model neurons achieved this by extending the inhibitory component of their receptive filters for more reverberant spaces, and did so in a frequency-dependent manner. These predicted effects were observed in the responses of auditory cortical neurons of ferrets in the same simulated reverberant environments. Together, these results suggest that auditory cortical neurons adapt to reverberation by adjusting their filtering properties in a manner consistent with dereverberation.

opencc-zeroJun 2022View details →

opencc-by-4.0Jun 2022View details →

Dataset, test programs and analysis scripts for the related paper "Effects of reverberation on speech intelligibility in noise for hearing-impaired listeners"

This dataset contains the data, test programs and analyses scripts used for a study submitted as a stage 2 registered report for Royal Society Open Science.

opencc-by-4.0Jul 2020View details →

Raw data and stimuli for assessing the perceived reverberation in different rooms for a set of musical instrument sounds

This set of data and sound stimuli was used in the study by Osses, McLachlan, and Kohlrausch (2020) to assess the perceived reverberation --including measurements and simulations-- for different instrument sounds in eight different rooms. The following are the directories that are provided: <ul> <li>00-Experiment-WAE_GM_201712: Web Audio Evaluation tool (WAE) used to run the listening experiment with 24 participants. Follow the instructions in README.txt to get the experiment running.</li> <li>01-Stimuli: Sound stimuli as exactly used during the listening experiments.</li> <li>02-Raw-data and 03-Results-summary: Outputs from WAE for each of the participants. The raw data contained in these XML files were extracted and stored in '03-Results-summary'</li> <li>04-Stimuli-9s-for-simulations: Same sounds as in '01-Stimuli' but truncated to have a duration of 9 s. These sounds were used as input to an implementation (Osses et al. 2017, 2020) of the model by van Dorp et al. (2013).</li> </ul> The paper figures can be reproduced in two MATLAB toolboxes: fastACI (script: publ_osses2020a_JASA_EL_figs.m) and AMT (script: exp_osses2020.m, availability as of 2023).

dryad36/100

Cortical adaptation to sound reverberation

Open the record for dataset details and reuse information.

publicJun 2022View details →

opencc-by-4.0Jul 2020View details →

Optimization of Convolution Reverberation (sound samples)

Sound samples of the paper "Optimization of Convolution Reverberation" by Sadjad Siddiq. To be published in the proceedings of DAFx20.

openother-ncApr 2018View details →

TUT Sound Events 2018 - Circular array, Reverberant and Synthetic Impulse Response Dataset

Tampere University of Technology (TUT) Sound Events 2018 - Circular array, Reverberant and Synthetic Impulse Response Dataset 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. 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. 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. 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.

openother-ncApr 2018View details →

TUT Sound Events 2018 - Ambisonic, Reverberant and Synthetic Impulse Response Dataset

Tampere University of Technology (TUT) Sound Events 2018 - Ambisonic, Reverberant and Synthetic Impulse Response Dataset 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. 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. 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. 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.