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306 results for “impulsivity”
Investigation of lightning ignition characteristics based on an impulse current generator.
<p>Lightning strike is an important ignition source of forest fires. Artificial lightning discharge is a method for studying lightning fires. However, there is not enough data on the ignition of combustible materials caused by artificial lightning discharge. Previous studies on lightning ignition have focused on the heating and ignition effects of long continuing current (LCC), but the function of the impulse current that occurs before the LCC has not been taken into account. In this paper, an impulse current generator of 8/20 μs was used to simulate the ignition effect of impulse current on conifer needle beds. Different current waveforms have different ignition characteristics. We compared five kinds of conifer needle beds. The average of the current needed to ignite the needle bed of <i>Larix gmelinii (Ruprecht) Kuzeneva</i><i> </i>was the smallest, and the average of the breakdown voltage was the smallest for the needle bed of <i>Pinus massoniana Lamb</i>. The total energy input to the conifer needle beds was fitted as a multiple log-linear regression model. The heating energy proportion value varies with different bulk densities, current amplitudes, and moisture contents. Based on this data, the heating energy of the impulse current transferred to the needles can be predicted. This information in conjunction with previous research on LCC was used to derive a lightning ignition prediction model of the full waveform for conifer needle beds.</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>
Dataset for "Activation of the mPFC-NAc pathway reduces motor impulsivity but does not affect risk-related decision-making in innately high-impulsive male rats"
<p><span>Attention-deficit/hyperactivity disorder (ADHD) and substance use disorders</span><span> (SUD) are characterized by exacerbated motor and risk-related impulsivities, which are associated with decreased cortical activity. In rodents, the medial prefrontal cortex (mPFC) and nucleus accumbens (NAc) have been separately implicated in impulsive behaviors, but studies on the specific role of the mPFC-NAc pathway in these behaviors are limited. Here, we investigated whether heightened impulsive behaviors are associated with reduced mPFC activity in rodents, and determined the involvement of the mPFC-NAc pathway in motor and risk-related impulsivities. We used the Roman High- (RHA) and Low-Avoidance (RLA) rat lines, which display divergent phenotypes in impulsivity. To investigate alterations in cortical activity in relation to impulsivity, regional brain glucose metabolism was measured using positron emission tomography and [<sup>18</sup>F]-fluorodeoxyglucose ([<sup>18</sup>F]FDG). Using chemogenetics, the activity of the mPFC-NAc pathway was either selectively activated in high-impulsive RHA rats or inhibited in low-impulsive RLA rats, and the effects of these manipulations on motor and risk-related impulsivity were concurrently assessed using the rat gambling task. We showed that basal [<sup>18</sup>F]FDG uptake was lower in the mPFC and NAc of RHA compared to RLA rats. Activation of the mPFC-NAc pathway in RHA rats reduced motor impulsivity, without affecting risk-related decision-making. Conversely, inhibition of the mPFC-NAc pathway had no effect in RLA rats. Our results suggest that the mPFC-NAc pathway controls motor impulsivity, but has limited involvement in risk-related decision-making. Our findings suggest that reducing fronto-striatal activity may help attenuate motor impulsivity in patients with impulse control dysregulation like ADHD or SUD.</span></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>
Dataset of two-impulse Earth–Moon transfers in a four-body model
<p>The Dataset.mat file contains data of all two-impulse Earth–Moon transfers in a four-body model generated in</p> <p>Topputo, F., "On optimal two-impulse Earth–Moon transfers in a four-body model". <em>Celestial Mechanics & Dynamical Astronomy, </em><strong>117</strong>, 279–313 (2013). https://doi.org/10.1007/s10569-013-9513-8</p> <p>The journal article and its supplementary material file are also provided.</p> <p>In Dataset.mat, each row represents a solution entry, with the elements:</p> <p><strong>Solution achieved after MS optimization</strong></p> <p>1) Transfer cost [adimensional]</p> <p>2) Transfer time [adimensional]</p> <p>3) n (multiple shooting nodes)</p> <p>4) ExitFlag (output of the optimization, see Matlab's fmincon)</p> <p>5) Boundary Condion Error (constraints violation, see Matlab's fmincon)</p> <p>6) t0 (initial time) [adimensional]</p> <p>7) tf (final time) [adimensional]</p> <p>8-11) Initial state (x0, y0, xv0, yv0) [adimensional]</p> <p>12-15) Final state (xf, yf, xvf, yvf) [adimensional]</p> <p><strong>Initial guess prior MS optimization</strong></p> <p>16) alpha (angle on departure circular orbit) [adimensional]</p> <p>17) vcoeff (initial velocity coefficient) [adimensional]</p> <p>18) t0 (initial time) [adimensional]</p> <p>19) dt (time of flight) [adimensional]</p> <p>To reproduce the solutions in the dataset it is enough to integrate the initial conditon (x0, y0, xv0, yv0) from the intilal time (t0) to the final time (tf) with the equations of motion given in the paper. Note: the instantaneous Sun angle is ωs * t.</p> <p>Refer to the paper for the detailed description of all parameters and constants adopted to generate the dataset of solutions.</p>
Examining the mediating role of damage control and cooperation on the relationship between self-image and behavioral impulsivity in online social media among adolescents
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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>
No evidence that visual impulses enhance the readout of retrieved long-term memory contents from EEG activity
<p>This is the EEG and behavioral data for the long-term memory ping study by Sander van Bree, Abbie Sarah Mackenzie, and Maria Wimber.</p> <p>Paper title: No evidence that visual impulses enhance the readout of retrieved long-term memory contents from EEG activity</p> <p>The behavioral data is named "behav_res_pp", which is the output of script 1 (s0_extractdata.m) on Github. The EEG data is named "pp_reorder" and it is the output of script 5 (s5_correctdata.m); i.e., it is both preprocessed and correctly formatted for the main analyses.</p> <p>Move the behavioral data to folder /data/behav_data/ and the EEG data to folder /data/eeg_data/</p> <p>For the analysis scripts and more information, check Github: https://github.com/sandervanbree/MemPing</p>
Dataset Questionnaire E-Commerce Uncovered Exploring Key Drivers of Consumer Impulse Buying Behavior
<p>The following dataset is a dataset from a study that investigated Perceived Ease of Use and Perceived Usefulness on Impulsive buying throught Attitude Towards E-commerce.</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.