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306 results for “impulsivity”
PsPM-SSNA_3: Skin conductance response to stimulation of the peripheral median nerve with electrical impulses
<p>This dataset includes skin conductance response (SCR) for each of 4 healthy unmedicated participants (4 females, age range 24-49 years) in response to repeated stimulation of the peripheral median nerve with single impulses at successively increasing frequencies. Prior to the experiment, mepivacaine was delivered to induce axillary nerve blockade.</p>
BIRD: Big Impulse Response Dataset
<p>BIRD is an open dataset that consists of 100,000 multichannel room impulse responses generated using the image method. This makes it the <strong>largest multichannel open dataset currently available</strong>. We provide some Python code that shows how to download and use this dataset to perform online data augmentation. The code is compatible with the PyTorch dataset class, which eases integration in existing deep learning projects based on this framework.</p>
Data from: Spatio-temporal dynamics of impulse responses to figure motion in optic flow neurons
White noise techniques have been used widely to investigate sensory systems in both vertebrates and invertebrates. White noise stimuli are powerful in their ability to rapidly generate data that help the experimenter decipher the spatio-temporal dynamics of neural and behavioral responses. One type of white noise stimuli, maximal length shift register sequences (m-sequences), have recently become particularly popular for extracting response kernels in insect motion vision. We here use such m-sequences to extract the impulse responses to figure motion in hoverfly lobula plate tangential cells (LPTCs). Figure motion is behaviorally important and many visually guided animals orient towards salient features in the surround. We show that LPTCs respond robustly to figure motion in the receptive field. The impulse response is scaled down in amplitude when the figure size is reduced, but its time course remains unaltered. However, a low contrast stimulus generates a slower response with a significantly longer time-to-peak and half-width. Impulse responses in females have a slower time-to-peak than males, but are otherwise similar. Finally we show that the shapes of the impulse response to a figure and a widefield stimulus are very similar, suggesting that the figure response could be coded by the same input as the widefield response.
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>
The Impact of User Generated Content (UGC) on Impulsive Buying in Live Streaming Marketing
<p><span>The internet is growing, which affects the business industry's ability to keep up with its development by doing live streaming marketing. Live streaming marketing makes consumers make impulsive purchases because consumers can interact with sellers in real time and see people's reviews through comments, making consumers' buying intentions higher. The aim of this study is to explain how user-generated content in live-streaming marketing acts as an impulsive buying factor. It can also be referred to as user-generated content. Other consumers create user-generated content to give their honest review of a product in a video, photo, or other format. This research uses quantitative methods. The data was collected through a questionnaire distributed in June–September 2024 to 154 customers who buy via live streaming. The data collection method used is purposive sampling; the data is then processed using Structural Equation Modeling (SEM) with Smart PLS as tool. There are six variables in this research, there are:<span> </span>user generated content, content authenticity, social interaction, emotional appeal, pleasure, and impulsive buying with seven hypotheses.<span> </span>The results showed that the value of user-generated content, social interaction, and content authenticity in live-streaming marketing has a significant effect on pleasure and emotional appeal so that it can impact impulsive buying for users.</span></p> <p><span><strong><em><span>Keywords: user generated content, live streaming marketing, impulsive buying, UGC, e-commerce</span></em></strong></span></p>
Tampere University Rotated Circular Array Impulse Response Dataset(TUNI-RCAIR)
<p>The TUNI-RCAIR dataset consists of impulse responses collected from different spaces in the Tampere university campus, Finland. </p> <p>Each folder represents a data collection environment, and it consists of impulse responses and recorded data in the form of wav files. Each space has its own three alphabet identifier, for example- Anechoic chamber is represented as AEC. The folder names indicate the space and corresponding identifier</p> <p>There are seven different data collection environments, and the distance between the source and receiver is varied thrice (0.7m, 1.4m, and 2.1m), which can be found in the names of the sub folders.</p> <p>Further details about the dataset, the characteristis of the space used for data collection, can be found in the page - <strong>https://tuni-rcair.github.io/</strong></p> <p>This dataset is accepted as a part of EUSIPCO 2021, the name of the corresponding paper is, "Tampere University Rotated Circular Array Dataset". The link to the paper will be updated.</p> <p>EUSIPCO paper DOI - https://doi.org/10.23919/EUSIPCO54536.2021.9616072</p> <p>For citation please use - A. V. Venkatakrishnan, P. Pertilä and M. Parviainen, "Tampere University Rotated Circular Array Dataset," 2021 29th European Signal Processing Conference (EUSIPCO), 2021, pp. 201-205, doi: 10.23919/EUSIPCO54536.2021.9616072.</p> <p> </p>
Human ISMRMRD datasets for "MaxGIRF: Image Reconstruction Incorporating Concomitant Field and Gradient Impulse Response Function Effects"
<p>Human axial and sagittal ISMRMRD datasets for "MaxGIRF: Image Reconstruction Incorporating Concomitant Field and Gradient Impulse Response Function Effects".</p> <p>Code to process and reconstruct the data is available here: <a href="https://github.com/usc-mrel/lowfield_maxgirf">https://github.com/usc-mrel/lowfield_maxgirf</a></p>
Phantom ISMRMRD datasets for "MaxGIRF: Image Reconstruction Incorporating Concomitant Field and Gradient Impulse Response Function Effects"
<p>Phantom ISMRMRD datasets for "MaxGIRF: Image Reconstruction Incorporating Concomitant Field and Gradient Impulse Response Function Effects".</p> <p>Code to process and reconstruct the data is available here: <a href="https://github.com/usc-mrel/lowfield_maxgirf">https://github.com/usc-mrel/lowfield_maxgirf</a></p>
TAU Spatial Room Impulse Response Database (TAU-SRIR DB)
<p><strong>DESCRIPTION</strong></p> <p>The <strong>TAU Spatial Room Impulse Response Database (TAU-SRIR DB)</strong> database contains spatial room impulse responses (SRIRs) captured in various spaces of Tampere University (TAU), Finland, for a fixed receiver position and multiple source positions per room, along with separate recordings of spatial ambient noise captured at the same recording point. The dataset is intended for emulation of spatial multichannel recordings for evaluation and/or training of multichannel processing algorithms in realistic reverberant conditions and over multiple rooms. The major distinct properties of the database compared to other databases of room impulse responses are:</p> <ul> <li>Capturing in a high resolution multichannel format (32 channels) from which multiple more limited application-specific formats can be derived (e.g. tetrahedral array, circular array, first-order Ambisonics, higher-order Ambisonics, binaural).</li> <li>Extraction of densely spaced SRIRs along measurement trajectories, allowing emulation of moving source scenarios.</li> <li>Multiple source distances, azimuths, and elevations from the receiver per room, allowing emulation of complex configurations for multi-source methods.</li> <li>Multiple rooms, allowing evaluation of methods at various acoustic conditions, and training of methods with the aim of generalization on different rooms.</li> </ul> <p>The RIRs were collected by staff of TAU between 12/2017 - 06/2018, and between 11/2019 - 1/2020. The data collection received funding from the European Research Council, grant agreement 637422 <a href="https://cordis.europa.eu/project/id/637422">EVERYSOUND</a>.</p> <p><strong><em>NOTE</em></strong><em>: This database is a work-in-progress. We intend to publish additional rooms, additional formats, and potentially higher-fidelity versions of the captured responses in the near future, as new versions of the database in this repository.</em></p> <p> </p> <p><strong>REPORT AND REFERENCE</strong></p> <p>A compact description of the dataset, recording setup, recording procedure, and extraction can be found in:</p> <p>Politis., Archontis, Adavanne, Sharath, & Virtanen, Tuomas (2020). <strong>A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection</strong>. In <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)</em>, Tokyo, Japan.</p> <p>available <a href="https://dcase.community/documents/workshop2020/proceedings/DCASE2020Workshop_Politis_88.pdf">here</a>. A more detailed report specifically focusing on the dataset collection and properties will follow.</p> <p> </p> <p><strong>AIM</strong></p> <p>The dataset can be used for generating multichannel or monophonic mixtures for testing or training of methods under realistic reverberation conditions, related to e.g. multichannel speech enhancement, acoustic scene analysis, and machine listening, among others. It is especially suitable for the follow application scenarios:</p> <ul> <li>monophonic and multichannal reverberant single- or multi-source speech in multi-room reverberant conditions</li> <li>monophonic and multichannel polyphonic sound events in multi-room reverberant conditions </li> <li>single-source and multi-source localization in multi-room reverberant conditions, in static or dynamic scenarios</li> <li>single-source and multi-source tracking in multi-room reverberant conditions, in static or dynamic scenarios</li> <li>sound event localization and detection in multi-room reverberant conditions, in static or dynamic scenarios</li> </ul> <p> </p> <p><strong>SPECIFICATIONS</strong></p> <p>The SRIRs were captured using an [Eigenmike](https://mhacoustics.com/products) spherical microphone array. A [Genelec G Three loudspeaker](https://www.genelec.com/g-three) was used to playback a maximum length sequence (MLS) around the Eigenmike. The SRIRs were obtained in the STFT domain using a least-squares regression between the known measurement signal (MLS) and far-field recording independently at each frequency. In this version of the dataset the SRIRs and ambient noise are downsampled to 24kHz for compactness.</p> <p>The currently published SRIR set was recorded at nine different indoor locations inside the Tampere University campus at Hervanta, Finland. Additionally, 30 minutes of ambient noise recordings were collected at the same locations with the IR recording setup unchanged. SRIR directions and distances differ with the room. Possible azimuths span the whole range of $\phi\in[-180,180)$, while the elevations span approximately a range between $\theta\in[-45,45]$ degrees. The currently shared measured spaces are as follows:</p> <ol> <li>Large open space in underground bomb shelter, with plastic-coated floor and rock walls. Ventilation noise. Circular source trajectory.</li> <li>Large open gym space. Ambience of people using weights and gym equipment in adjacent rooms. Circular source trajectory.</li> <li>Small classroom (PB132) with group work tables and carpet flooring. Ventilation noise. Circular source trajectory.</li> <li>Meeting room (PC226) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory.</li> <li>Lecture hall (SA203) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory.</li> <li>Small classroom (SC203) with group work tables and carpet flooring. Ventilation noise. Linear source trajectory.</li> <li>Large classroom (SE203) with hard floor and rows of desks. Ventilation noise. Linear source trajectory.</li> <li>Lecture hall (TB103) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory.</li> <li>Meeting room (TC352) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory.</li> </ol> <p>The measurement trajectories were organised in groups, with each group being specified by a circular or linear trace at the floor at a certain distance from the z-axis of the microphone. For circular trajectories two ranges were measured, a <em>close</em> and a <em>far</em> one, except room TC352, where the same range was measured twice, but with different furniture configuration and open or closed doors. For linear trajectories also two ranges were measured, <em>close</em> and <em>far</em>, but with linear paths at either side of the array, resulting in 4 unique trajectory groups, with the exception of room SA203 where 3 ranges were measured resulting on 6 trajectory groups. Linear trajectory groups are always parallel to each other, in the same room.</p> <p>Each trajectory group had multiple measurement trajectories, following the same floor path, but with the source at different heights. </p> <p>The SRIRs are extracted from the noise recordings of the slowly moving source across those trajectories, at an angular spacing of approximately every 1 degree from the microphone. Instead of extracting SRIRs at equally spaced points along the path (e.g. every 20cm), this extraction scheme was found more practical for synthesis purposes, making emulation of moving sources at an approximately constant angular speed easier.</p> <p>More details on the trajectory geometries can be found in the <strong>README</strong> file and the <strong>measinfo.mat</strong> file.</p> <p> </p> <p><strong>RECORDING FORMATS</strong></p> <p>As with the DCASE2019-2021 datasets, currently the database is provided in two formats, first-order Ambisonics, and a tetrahedral microphone array - both derived from the Eigenmike 32-channel recordings. For more details on the format specifications, check the README. </p> <p>We intend to add additional formats of the database, of both higher resolution (e.g. higher-order Ambisonics), or lower resolution (e.g. binaural).</p> <p> </p> <p><strong>REFERENCE DOAs</strong></p> <p>For each extracted RIR across a measurement trajectory there is a direction-of-arrival (DOA) associated with it, which can be used as the reference direction for sound source spatialized using this RIR, for training or evaluation purposes. The DOAs were determined acoustically from the extracted RIRs, by windowing the direct sound part and applying a broadband version of the MUSIC localization algorithm on the windowed multichannel signal.</p> <p>The DOAs are provided as Cartesian components [x, y, z] of unit length vectors.</p> <p> </p> <p><strong>SCENE GENERATOR</strong></p> <p>A set of routines is shared, here termed <em>scene generator</em>, that can spatialize a bank of sound samples using the SRIRs and noise recordings of this library, to emulate scenes for the two target formats. The code is similar to the one used to generate the <a href="https://doi.org/10.5281/zenodo.5476980"><strong>TAU-NIGENS Spatial Sound Events 2021</strong></a> dataset, and has been ported to Python from the original version written in Matlab.</p> <p>The generator can be found [**<strong>here</strong>**](https://github.com/danielkrause/DCASE2022-data-generator), along with more details on its use. </p> <p>The generator at the moment is set to work with the <a href="https://zenodo.org/record/2535878">NIGENS</a> sound event sample database, and the <a href="https://zenodo.org/record/4060432">FSD50K</a> sound event database, but additional sample banks can be added with small modifications.</p> <p>The dataset together with the generator has been used by the authors in the following public challenges:</p> <p>- <a href="https://dcase.community/challenge2019/task-sound-event-localization-and-detection">DCASE 2019 Challenge Task 3</a>, to generate the <strong>TAU Spatial Sound Events 2019</strong> dataset (<a href="https://doi.org/10.5281/zenodo.2599196">development</a>/<a href="https://doi.org/10.5281/zenodo.3377088">evaluation</a>)</p> <p>- <a href="https://dcase.community/challenge2020/task-sound-event-localization-and-detection">DCASE 2020 Challenge Task 3</a>, to generate the <a href="https://doi.org/10.5281/zenodo.4064792"><strong>TAU-NIGENS Spatial Sound Events 2020</strong></a> dataset</p> <p>- <a href="https://dcase.community/challenge2021/task-sound-event-localization-and-detection">DCASE2021 Challenge Task 3</a>, to generate the <a href="https://doi.org/10.5281/zenodo.5476980"><strong>TAU-NIGENS Spatial Sound Events 2021</strong></a> dataset</p> <p>- <a href="https://dcase.community/challenge2022/task-sound-event-localization-and-detection">DCASE2022 Challenge Task 3</a>, to generate additional <a href="https://doi.org/10.5281/zenodo.6406873"><strong>SELD synthetic mixtures for training the task baseline</strong></a></p> <p><em><strong>NOTE</strong>: The current version of the generator is work-in-progress, with some code being quite "rough". If something does not work as intended or it is not clear what certain parts do, please contact us.</em></p> <p> </p> <p><strong>DATASET STRUCTURE</strong></p> <p>The dataset contains a folder of the SRIRs (<strong>TAU-SRIR_DB</strong>), with all the SRIRs per room in a single MAT file. The file <strong>rirdata.mat</strong> contains some general information such as sample rate, format specifications, and most importantly the DOAs of every extracted SRIR. The file <strong>measinfo.mat</strong> contains measurement and recording information in each room. Finally, the dataset contains a folder of spatial ambient noise recordings (<strong>TAU-SNoise_DB</strong>), with one subfolder per room having two audio recordings fo the spatial ambience, one for each format, FOA or MIC. For more information on how to SRIRs and DOAs are organized, check the README.</p> <p> </p> <p><strong>DOWNLOAD</strong></p> <p>The files <em>TAU-SRIR_DB.z01</em>, ..., <em>TAU-SRIR_DB.zip</em> contain the SRIRs and measurement info files.</p> <p>The files <em>TAU-SNoise_DB.z01</em>, ..., <em>TAU-SNoise_DB.zip</em> contain the ambient noise recordings.</p> <p>Download the zip files and use your preferred compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal:</p> <p>Combine the split archive to a single archive:</p> <pre><code>zip -s 0 split.zip --out single.zip</code></pre> <p>Extract the single archive using unzip:</p> <pre><code>unzip single.zip</code></pre> <p> </p> <p><strong>LICENSE</strong></p> <p>The database is published under a custom **<strong>open non-commercial with attribution</strong>** license. It can be found in the `LICENSE.txt` file that accompanies the data.</p>
Dataset for publication: "Empirical Characterization of Cable Effects on a Reference Lightning Impulse Voltage Divider"
<p>Dataset for publication: "Empirical Characterization of Cable Effects on a Reference Lightning Impulse Voltage Divider". Excel file contains the data for Figures 5 and 6.</p>
Single impulse spot stimulation of a cerebellar column in WT and MLI-KO conditions
<p>A 300x200um volume of the cerebellar cortex was stimulated, and spherical spot with diameter 50um was stimulated with a single impulse. The impulse causes exactly 1 presynaptic release event at each mossy fiber terminal (glomerulus) inside of the sphere.</p>
[3/3] Patch clamp of a Purkinje cell in a simulated cerebellar cortex model during single impulse stimulation
<p>Patch clamp of a Purkinje cell in a simulated cerebellar cortex model during single impulse stimulation</p>
[2/3] Patch clamp of a Purkinje cell in a simulated cerebellar cortex model during single impulse stimulation
<p>Patch clamp of a Purkinje cell in a simulated cerebellar cortex model during single impulse stimulation</p>
[1/3] Patch clamp of a Purkinje cell in a simulated cerebellar cortex model during single impulse stimulation
<p>Patch clamp of a Purkinje cell in a simulated cerebellar cortex model during single impulse stimulation</p>
Dataset of Room Impulse Responses from Baffled Microphone Arrays and Sound Sources at Three Elevations
<p>This data set contains a collection of impulse responses (stored in SOFA format) from <em>spherical microphone arrays</em> (<strong>SMA</strong>s), <em>equatorial microphone arrays</em> (<strong>EMA</strong>s), and <em>non-spherical microphone arrays</em> (<strong>XMA</strong>s). Thereby, impulse response sets are provided for each array type at various spatial resolutions, for a loudspeaker sound source at three source elevations, and in four diverse acoustic environments (see <strong>DATA</strong> section for a full description).</p> <p>The original purpose of the microphone array data is the binaural rendering in the <em>spherical harmonics</em> (<strong>SH</strong>) domain into ear signals for high-fidelity reproduction of the acoustic scenario via headphones. Therefore, <em>binaural room impulse responses</em> (<strong>BRIR</strong>s) for 360 horizontal head orientations of a <em>G.R.A.S KEMAR</em> acoustic dummy head are provided as a reference for each scenario.</p> <p>Please contact the authors for questions or additional information regarding the room setups and utilized measurement devices.</p> <p> </p> <p><strong>======<br> DATA<br>======</strong></p> <p>This archive contains the processed impulse response sets of various measurement configurations, as described in this section.</p> <p>Directory "resources/ARIR_processed/":</p> <ul> <li>Post-processed SMA and EMA impulse responses <ul> <li><strong>"_SMA*_"</strong> or <strong>"_EMA*_"</strong> in the file name</li> <li>In SOFA format with <em>"SingleRoomSRIR"</em> convention</li> <li>From 1x <em>DPA 4060</em> microphone flush mounted in a wooden spherical scattering body with an 8.5 cm radius</li> <li>High-resolution data (measured sequentially on VariSphear turntable with two degrees-of-freedom rotations): <ul> <li>Hall: <strong>1202</strong> <strong>channels</strong> (Lebedev grid) for maximum SH order 29</li> <li>Others: <strong>2702</strong> <strong>channels</strong> (Lebedev grid) for maximum SH order 44</li> </ul> </li> <li>Lower-resolution data via subsampling in the SH domain (arbitrary sampling grids and lower target orders can be achieved): <ul> <li>SH order 29: <strong>1742 channels</strong> (t-design grid) for SMA; <strong>59</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 12: <strong>314</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>25</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 8: <strong>146</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>17</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 4: <strong>42</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>9</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 2: <strong>14</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>5</strong> <strong>channels</strong> (equiangular grid) for EMA</li> <li>SH order 1: <strong>6</strong> <strong>channels</strong> (t-design grid) for SMA; <strong>3</strong> <strong>channels</strong> (equiangular grid) for EMA</li> </ul> </li> </ul> </li> <li>Post-processed XMA impulse responses <ul> <li><strong>"_XMA*_"</strong> in the file name</li> <li>In SOFA format with <em>"SingleRoomSRIR"</em> convention</li> <li>From 18x <em>Rode Lavalier GO</em> microphone mounted in an elastic band on a wooden head-shaped scattering body (7.5 cm to 10.5 cm radius)</li> <li>High-resolution data (measured simultaneously): <ul> <li><strong>18 channels</strong> for maximum SH order 8</li> </ul> </li> <li>Lower-resolution data via integer subsets of microphones: <ul> <li>SH order 4: <strong>9 channels</strong></li> <li>SH order 2: <strong>6 channels</strong></li> </ul> </li> <li>Anechoic: For 360 horizontal scattering body orientations (measured sequentially on a VariSphear turntable with azimuth in 1-degree steps)</li> <li>Rooms: For 36 horizontal scattering body orientations (measured sequentially on VariSphear turntable with azimuth in 10-degree steps)</li> </ul> </li> <li>Generated XMA calibration filters and equalization filters <ul> <li><strong>"_x_nm_"</strong> and <strong>"_e_nm_"</strong> in the file name</li> <li>In proprietary Matlab format</li> <li>Time-domain representation of filters in the respective orders of "real" spherical harmonics</li> </ul> </li> <li>Post-processed binaural impulse responses <ul> <li><strong>"_KEMAR_"</strong> in the file name</li> <li>In SOFA format with <em>"SingleRoomSRIR"</em> convention</li> <li>From <em>G.R.A.S KEMAR</em> dummy head with large pinna</li> <li>For 360 horizontal head orientations (measured sequentially on VariSphear turntable with azimuth in 1-degree steps)</li> </ul> </li> <li>Thereby, impulse response sets are included for five acoustic environments <ul> <li><strong>"Simulation_"</strong>: Anechoic simulation of a plane wave impinging from the frontal direction on the array (SMA and EMA only)</li> <li><strong>"Anechoic_"</strong>: Anechoic measurement of a <em>Genelec 8030A</em> loudspeaker at the same height of the array</li> <li>"<strong>LabDry_"</strong>: Room measurement in an acoustically damped laboratory of a <em>Genelec 8030A</em> loudspeaker at three different source heights (the direct floor reflection is attenuated with an additional porous absorber but otherwise identical to the following condition)</li> <li><strong>"LabWet_"</strong>: Room measurement in an acoustically damped laboratory of a <em>Genelec 8030A</em> loudspeaker at three different source heights (the direct reflection is not obstructed from the hard concrete floor, but otherwise identical to the former condition)</li> <li><strong>"Hall_"</strong>: Room measurement in a very reverberant hall of a <em>Genelec 8030A</em> loudspeaker at three different source heights</li> </ul> </li> <li>Thereby, the room impulse response sets are included for three relative source elevations (from placing the loudspeaker to varying heights on the same vertical axis) <ul> <li><strong>"_SrcHigh"</strong>: The source is located above the horizon of the receiver</li> <li>"<strong>_SrcEar"</strong>: The source and receiver are located at the same height</li> <li><strong>"_SrcLow"</strong>: The source is located below the horizon of the receiver</li> </ul> </li> <li>Additionally, anechoic impulse responses of the measurement loudspeaker and the utilized microphones are included <ul> <li><strong>"Anechoic_MicSMAnoTape_"</strong>: SMA measurement microphone without the applied tape (the source was compensated)</li> <li><strong>"Anechoic_MicSMAwithTape_"</strong>: SMA measurement microphone with the applied tape (the source was compensated)</li> <li><strong>"Anechoic_MicXMAmic19_"</strong>: XMA measurement microphone (the source was compensated)</li> <li><strong>"Anechoic_SrcFreeField_"</strong>: Measurement source (on-axis) (the influence of the utilized high-quality free-field measurement microphone can be neglected)</li> <li><strong>"Anechoic_SrcFreeField+MicSMAnoTape_"</strong>: Measurement source and SMA measurement microphone without the tape applied</li> <li><strong>"Anechoic_SrcFreeField+MicSMAwithTape_"</strong>: Measurement source and SMA measurement microphone with the tape applied</li> <li><strong>"Anechoic_SrcFreeField+MicXMAmic19_"</strong>: Measurement source and XMA measurement microphone</li> <li>Overall, the resulting impulse response sets contain the following compensations (including exact compensation of the phase/time behavior): <ul> <li>Anechoic KEMAR: Source</li> <li>Anechoic SMA/EMA/XMA: Source and array microphones</li> <li>Rooms KEMAR: None</li> <li>Rooms SMA/EMA/XMA: Array microphones</li> <li>There is the option to compensate for the source's on-axis response in the room measurement data. However, the direction-dependent directivity of the loudspeaker cannot be compensated. Therefore, we decided not to compensate for the source in the room measurement data since the on-axis frequency response of the utilized loudspeaker is reasonably flat.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p><strong>===========<br> DATA_RAW<br>===========</strong></p> <p><strong>This archive is too large to be uploaded to Zotero (around 77.5 GB). Please get in touch with the authors to request the data.</strong></p> <p>The archive contains the raw acoustic data of all measurement configurations captured by the measurement scripts (see section <strong>CODE_AND_PLOTS</strong>). The data yields the final impulse responses (see section <strong>DATA</strong>), as described in this section.</p> <p>Directory "resources/ARIR_raw/":</p> <ul> <li>Subdirectories by room and source position containing the raw SMA, XMA, and KEMAR acoustic measurement data</li> <li>In proprietary Matlab format, separate for every measurement position of each configuration</li> <li>Each data file contains extensive metadata, e.g., describing the utilized hardware devices, input/output ports, and descriptions.</li> <li>Each data file contains the raw utilized exponential sweep signal and the resulting captured microphone signals. Each impulse response may be recomputed with alternative deconvolution and post-processing parameters.</li> </ul> <p>Directory "resources/ARIR_raw/Logs_temp_humidity/":</p> <ul> <li>Air temperature and humidity data were captured in 5-second intervals during all acoustic measurements</li> <li>In CSV format (automatically loaded and included in the final impulse response sets as part of the measurement post-processing; see section <strong>CODE_AND_PLOTS</strong>)</li> <li>This data is not further utilized at the moment but seemed worthwhile to capture since some acoustic measurements (particularly the high-resolution SMA data sets) were conducted over multiple hours.</li> </ul> <p> </p> <p><strong>==================<br> CODE_AND_PLOTS<br>==================</strong></p> <p>This archive contains the code required to gather the raw acoustic measurement data (see section <strong>DATA_RAW</strong>), the code to post-process and yield the final impulse response data (see section <strong>DATA</strong>), and the resulting plots as described in this section.</p> <p>Directory "dependencies/":</p> <ul> <li>Matlab and Python functions that are utilized in the code</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 code header.</li> </ul> <p>Directory "plots/":</p> <ul> <li>Plots that were exported (and that may be regenerated) by the following scripts to validate different stages of the data simulation, measurement, and subsampling.</li> </ul> <p>Shell script "x1_Start_Jupyter.sh":</p> <ul> <li>Prepare a Python environment with the required tools described as dependencies.</li> <li>Activate the prepared Python environment to perform impulse response measurements using Jupyter Notebooks setup for different acoustic settings.</li> </ul> <p>Python Jupyter notebook "x1a_Measure_Microphones.ipynb":</p> <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Perform a series of acoustic measurements of all utilized microphones in an anechoic environment.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> <p>Python Jupyter notebook "x1b_Measure_BRIRs.ipynb":</p> <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Generate a horizontal grid of measurement orientations for the VariSphear turntable according to the desired dummy head orientations.</li> <li>Perform a series of acoustic measurements of the dummy head at the pre-defined grid in anechoic and various room environments.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> <p>Python Jupyter notebook "x1c_Measure_SMAs.ipynb":</p> <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Generate a spherical grid of measurement orientations for the VariSphear turntable according to the desired SMA sampling grid.</li> <li>Perform a series of acoustic measurements of the SMA microphone at the pre-defined grid in anechoic and various room environments.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> <p>Python Jupyter notebook "x1d_Measure_XMAs.ipynb":</p> <ul> <li>Setup and test the utilized acoustic measurement hardware.</li> <li>Generate a horizontal grid of measurement orientations for the VariSphear turntable according to the desired scattering body orientations.</li> <li>Perform a series of acoustic measurements of the XMA microphones at the pre-defined grid in anechoic and various room environments.</li> <li>Export the raw acoustic data and processed impulse responses.</li> </ul> <p>Matlab script "x1e_Simulate_SMAs.m":</p> <ul> <li>Simulate a plane wave impinging from an arbitrary direction on SMAs and EMAs with a desired sampling grid in an anechoic environment.</li> <li>The simulations are helpful to evaluate the rendering method and to investigate the influence of different sampling grids and equalization methods on the rendered binaural signals.</li> </ul> <p>Matlab script "x2_Gather_And_Plot_Measurements.m":</p> <ul> <li>Gather the stored single files with individually measured impulse responses and the according metadata into a combined data set.</li> <li>The initial impulse responses can be recomputed with pre- and post-processing parameters tuned towards the specific acoustic scenario, including compensation of provided source and receiver impulse responses.</li> <li>Many plots may be generated during the processing to validate the input and output data.</li> </ul> <p>Matlab script "x2a_Compare_Measurement_Lengths.m":</p> <ul> <li>Compare the length of the resulting impulse responses of designated measurement configurations.</li> <li>This may be helpful for the tuning of pre-processing and post-processing parameters of the measured impulse responses.</li> </ul> <p>Matlab script "x3_Subsample_Measurements.m":</p> <ul> <li>Spatially subsample a high-resolution directional impulse response data set into a different (lower-resolution) sampling grid in the spherical harmonics domain.</li> <li>This is suitable for array and HRIR data sets.</li> <li>The script also compares the subsampled data against a reference set if available. In the current data set, an evaluation is performed for an anechoic simulation and a room measurement of an SMA at SH order 8.</li> </ul> <p>Matlab script "x3a_Gather_XMA_Measurements.m":</p> <ul> <li> <p>Transform anechoic XMA measurement data from SOFA into the data format required by the processing scripts to calculate the respective calibration and equalization filters.</p> </li> </ul> <p>Readme file "x3b_Generate_XMA_Filters.txt":</p> <ul> <li>The code for this functionality follows the publication [1] but is currently not polished enough for publication. Please contact Jens Ahrens (jens.ahrens@chalmers.se) for questions regarding this functionality.</li> <li>[1] J. Ahrens, H. Helmholz, D. Lou Alon, and S. V. Amengual Garí, “Spherical Harmonic Decomposition of a Sound Field Using Microphones on a Circumferential Contour Around a Non-Spherical Baffle,” <em>IEEE/ACM Trans. Audio, Speech, Lang. Process.</em>, vol. 30, pp. 3110–3119, 2022, doi: 10.1109/TASLP.2022.3209940.</li> </ul> <p>Matlab script "x3c_Gather_XMA_Filters.m":</p> <ul> <li>Rename the files containing the computed calibration and equalization filters into a suitable convention for this collection of scripts.</li> <li>The generated name includes an incremental index to track different versions of provided filter sets.</li> </ul> <p>Matlab script "x3d_Compare_XMA_Filters.m":</p> <ul> <li> <p>Generate various time domain and frequency domain plots to compare different versions of the generated XMA calibration and equalization filters.</p> </li> </ul> <p> </p> <p><strong>================<br> DOCUMENTATION<br>================</strong></p> <p>This archive contains additional documentation of the setups and processes while conducting the acoustic measurements, as described in this section.</p> <p>Directory "documentation/":</p> <ul> <li>Various photographs of the different room, source, and receiver arrangements of the data set</li> <li>The room dimensions and source and receiver positions are documented in the form of the original measurement notes (this may be improved in the future).</li> </ul> <p> </p>
In-Car McVAMPIRE – In-Car Multichannel Varying Mouth Position Impulse Response Dataset
<p>This dataset contains impulse responses (IRs) that were recorded in a minivan with eight seats arranged in three seat rows. The IRs were captured with 14 distributed overhead microphones using a mouth simulator at eight passenger seat positions with eleven orientations each. In addition, the dataset contains IRs measured with six built-in car loudspeakers as well as driving noise recordings at various speeds.</p> <p>This dataset supplements the <a href="https://doi.org/10.5281/zenodo.12818781">Anechoic McVAMPIRE</a> dataset which was captured with an identical microphone setup in an anechoic chamber. Both datasets can be used to simulate speech in a car from different seats with different speaker orientations including the loudspeaker-enclosure-microphone (LEM) system under anechoic or realistic, reverberant conditions.</p>
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>
IMPULSE: Integrate Public Metadata Underneath professional Library SErvices
<p>This repository contains the results of our experimental evaluation conducted with IMPULSE (<a href="https://github.com/t-blume/impulse">https://github.com/t-blume/impulse</a>). The datasets are subsets of the Billion Triple Challenge Dataset 2014 (<a href="http://km.aifb.kit.edu/projects/btc-2014/">http://km.aifb.kit.edu/projects/btc-2014/</a>) containing only bibliographic metadata.</p>
Dataset of simulated room impulse responses in three coupled rooms
<p>This dataset accompanies the publication</p> <blockquote> <div> <div> <div> <p>Georg Götz, Teodors Kerimovs, Sebastian J. Schlecht, and Ville Pulkki. Dynamic late reverberation rendering using the common-slope model. In Proceedings of the AES 6th International Conference on Audio for Games, Tokyo, Japan, April 2024.</p> </div> </div> </div> </blockquote> <div> <div> <div> <p> </p> <div> <div> <div> <p>The dataset includes room acoustic simulations conducted with the hybrid simulation suite Treble, using a transition frequency of approximately 750 Hz between wave-based and GA simulation. We simulated the coupled room geometry depicted in the file room_geometry2.pdf. The orange × indicates the source position, and receivers were uniformly distributed on the xy-plane with 0.3 m resolution. Each room has a height of 3 m and exhibits a uniform absorption distribution. Room R2 is the most reverberant with an absorption coefficient similar to concrete (αR2 = 0.01), whereas R1 and R3 are significantly less reverberant with αR1 = 0.2 and αR3 = 0.1, respectively.</p> <p>The dataset also includes the common-slope analysis results for the omnidirectional responses and also for the sector-based analysis as described in the paper. Please also refer to the following paper for more details on the common-slope analysis:</p> <blockquote> <p>Georg Götz, Sebastian J. Schlecht, and Ville Pulkki. Common-slope modeling of late reverberation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 31, pp. 3945–3957, September 2023. doi: <a href="https://doi.org/10.1109/TASLP.2023.3317572" target="_blank" rel="noopener">10.1109/TASLP.2023.3317572</a></p> </blockquote> </div> </div> </div> </div> </div> </div>
Dataset for "Dissociable Roles of the mPFC-to-VTA pathway in the control od Impulsive action and Risk-Related Decision-Making in Roman High- and Low-Avoidance Rats"
<p>This dataset corresponds to the study "Dissociable Roles of the mPFC-to-VTA Pathway in the Control of Impulsive Action and Risk-Related Decision-Making in Roman High- and Low-Avoidance Rats". In this study, we used Positron Emission Tomography with [18F]-Fluorodeoxyglucose to evaluate brain metabolic activity in Roman High- (RHA) and Low-avoidance (RLA) rats, which exhibit innate differences in impulsivity. Notably, we used a viral-based intersectional chemogenetic strategy to isolate the role of the mPFC-to-VTA pathway in controlling impulsive behaviors. We selectively activated the mPFC-to-VTA pathway in RHA rats and inhibited it in RLA rats, assessing the effects on impulsive action and RDM in the rat gambling task. Our results showed that RHA rats displayed higher impulsive action, less optimal decision-making, and lower cortical activity than RLA rats at baseline. Chemogenetic activation of the mPFC-to-VTA pathway reduced impulsive action in RHA rats, whereas chemogenetic inhibition had the opposite effect in RLA rats. However, these manipulations did not affect RDM. Our findings suggest a dissociable role of the mPFC-to-VTA pathway in impulsive action and RDM, highlighting its potential as a target for investigating impulsivity-related disorders.</p> <p><strong>Contributions for usage of this data in publications:</strong></p> <p>If you publish any work using these data, please cite this repository and the associated publication.</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.