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Ambisonics Recordings of Typical Environments

ARTE
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The ARTE database, so far, contains 13 acoustic environments that were recorded with a purpose-built 62-channel microphone array in various locations around Sydney (Australia), and was decoded into the higher-order Ambisonics (HOA) format. For each acoustic environment the following files are provided: HOA environment files: The recorded environments were decoded into 31mixed-order HOA channels and saved as WAV-files with a sampling frequency of 44.1 kHz and 32 bits per sample. Thereby, channels 1-25 refer to the 3D HOA periphonic (horizontal) components up to the order of M = 4, and channels 26-31 refer to additional sectorial 2D components (i.e., m = n) up to the order of M = 7. HOA RIR files: In each environment, Room Impulse Responses (RIRs) were measured with a Tannoy V8 dual-concentric loudspeaker at a number of positions relative to the microphone array. Currently, only a single RIR is provided in each environment which was measured with a loudspeaker in front of the microphone array (0 degree azimuth) at a distance of 1.3 m. Similar to the noise files, the RIRs are provided as 31-channel WAV-files with a sampling frequency of 44.1 kHz and 32 bits per sample. In addition to the “standard” RIR, a second version is provided in which the RIR was split into a direct sound (DS) component as well as a reverberation component (REV). The separated version of the RIR can be useful for enhancing the directionality (and frequency response) of the direct sound by decoding it into a single loudspeaker channel (i.e., a loudspeaker at an azimuth angle of 0 degrees) and then adding it back to the reverberant component, which is decoded normally. This process has been shown to be particularly useful when evaluating the benefit provided by directional signal enhancement methods (e.g., beamformers) in hearing aids. Binaural environment files: The HOA noise files were transformed into binaural headphone signals by simulating their playback via a 41-channel loudspeaker array to the in-ear microphones of a calibrated Bruel & Kjaer Head and Torso Simulator (HATS type 4128C). These binaural signals are provided in two versions: (a) an unprocessed version that needs to be presented via headphones that are equalized using an artificial ear and (b) a version that can be directly played back via any diffuse-field equalized headphones. Binaural RIRs: The HOA RIRs were transformed into binaural RIRs in the same way as the HOA noise files (see above) and were saved both unequalized and diffuse-field equalized. Basic acoustic measures: A number of basic acoustic measures are provided by a separate PDF-file for each environment, including: (a) unweighted sound pressure levels (dB SPL), (b) A-weighted sound pressure levels (dBA), (c) reverberation time (RT60), (d) third-octave power spectra in dB SPL, (e) temporal envelopes, (f) amplitude modulation spectra, and (g) directional characteristics in the horizontal plane. The acoustic measures were derived by simulating the playback of the MOA noise files (and RIRs) via a 41-channel loudspeaker array to a calibrated omni-directional 1/4” GRAS microphone (Type 46BL). Apart from the acoustic environment specific files, the ARTE database includes a number of MatlabTM functions that help decoding the provided HOA files into a format that can be played back via a given loudspeaker array, and includes a number of examples. Further technical details are described in Weisser, et al. (2019). Supporting material: The provided MatlabTM scripts and examples assume that the downloaded files are organized in a specific directory structure. This structure is generated automatically when downloading (and unzipping) the main zip-file (ARTE database downloas.7z). Note that this zip-file contains all required functions except the MOA and binaural sound files and RIRs. Due to their file size (about 10 GB in total), these sound files should be downloaded, one by one, from the individual links provided below. Some notes on calibration: All HOA noise files were normalized in the same way such that they correctly maintain their original differences in sound pressure level. Hence, once the sensitivity of the loudspeaker playback system is known, the same playback gain must be applied to all noise files. Even though this playback gain can be derived using any of the provided noise files, the easiest noise file for calibrating the loudspeaker playback system is the provided diffuse noise due to its steady-state behavior. Given that most playback environments contain significant low-frequency background noise, and loudspeakers have different low-frequency roll-offs, the provided A-weighted sound pressure levels should be best used for calibration. Also, it is assumed here that all loudspeakers in the playback array have the same distance to the listener, identical sensitivity, and a flat frequency response. If this is not the case the loudspeakers need to be equalized individually. Also, reverberation of the playback room should be as low as possible. Acknowledgement: The development of the ARTE database was financially supported by the HEARing CRC, established and supported under the Cooperative Research Centres Program – an initiative of the Australian Government, and the Oticon foundation. References: Weisser, A., Buchholz, J. M., Oreinos, C., Badajoz-Davila, J., Galloway, J., Beechey, T., Keidser, G. (2019). The Ambisonics Recordings of Typical Environments (ARTE) database. Acta Acustica united with Acustica. (see provided pdf-file)

관련 논문 31편

ArtEmis

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ArtEmis is a large-scale dataset aimed at providing a detailed understanding of the interplay between visual content, its emotional effect, and explanations for the latter in language. In contrast to most existing annotation datasets in computer vision, this dataset focuses on the affective experience triggered by visual artworks an the annotators were asked to indicate the dominant emotion they feel for a given image and, crucially, to also provide a grounded verbal explanation for their emotion choice. This leads to a rich set of signals for both the objective content and the affective impact of an image, creating associations with abstract concepts (e.g., “freedom” or “love”), or references that go beyond what is directly visible, including visual similes and metaphors, or subjective references to personal experiences. This dataset focuses on visual art (e.g., paintings, artistic photographs) as it is a prime example of imagery created to elicit emotional responses from its viewers. ArtEmis contains 439K emotion attributions and explanations from humans, on 81K artworks from WikiArt. Paper: ArtEmis: Affective Language for Visual Art Source: ArtEmis Dataest Image Source: ArtEmis Dataest

관련 논문 6편

Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset

ARCADE
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ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 2 consist of two folders with 300 images in each of them as well as annotations. ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1 consists of two datasets of XCA images for each of two tasks of ARCADE challenge. The first task includes in total 1200 coronary vessel tree images, which are divided into train(1000) and validation(200) groups, images for training are followed with annotations, depicting the division of a heart into 26 different regions based on the Syntax Score methodology[1]. Similarly, the second task includes a different set of 1200 images with same train-val division proportion with annotated regions containing atherosclerotic plaques. This dataset, carefully annotated by medical experts, enables scientists to actively contribute towards the advancement of an automated risk assessment system for patients with CAD.

관련 논문 2편

Funding Successful Projects on Kickstarter

kickstarter
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Kickstarter is a community of more than 10 million people comprising of creative, tech enthusiasts who help in bringing creative project to life. Till now, more than $3 billion dollars have been contributed by the members in fueling creative projects. The projects can be literally anything – a device, a game, an app, a film etc. Kickstarter works on all or nothing basis i.e if a project doesn’t meet it goal, the project owner gets nothing. For example: if a projects’s goal is \$500. Even if it gets funded till \$499, the project won’t be a success. Recently, Kickstarter released its public data repository to allow researchers and enthusiasts like us to help them solve a problem. Will a project get fully funded ?

관련 논문 1편

bovine pulmonary artery endothelial cells

BPAEC
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This dataset contains the confocal fluorescence microscopy images of nucleus, actin and mitochondria, where each clear image corresponds to 6 out-of-focus images with different degree of blurring. Images acquired below and above the optimal focal plane are blurry and out-of-focus images. In detail, scans were acquired in z-stack of 15 layers spanning the depth (8.4 μm) with 0.6 μm between each slice, z = 7 is the optimal focal plane, z = 1–6 are below the focal plane, z = 8–15 are above the focal plane. We make layers from z = 4 to z = 10 publicly available. Their visual variations in 3-dimensional structure can be negligible. In the end, datasets of actin and nucleus contain 100 in-focus images and 600 out-of-focus images respectively, dataset of mitochondria contain 97 in-focus images and 582 out-of-focus images. Each image is composed of 1024 × 1024 pixels in 8-bit JPG format.

관련 논문 1편

FIRESTARTER 2 - dataset and notebooks

FIRESTARTER 2 - dataset and notebooks 설명 보기

Data used in the paper "FIRESTARTER 2: Dynamic Code Generation for Processor Stress Tests", as well as notebooks to generate plots.

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How do I book a private jet charter through Expedia?

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관련 논문 0편

https://huggingface.co/datasets/SSS/SC-artext

SC-artext
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Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset