Signal Processing
The following dataset consists of utterances, recorded using 24 volunteers raised in the Province of Manitoba, Canada. To provide a repeatable set of test words that would cover all of the phonemes, the Edinburg Machine Readable Phonetic Alphabet (MRPA) [KiGr08], consisting of 44 words is used. Each recording consists of one word uttered by the volunteer and recorded in one continuous session.
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This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/
The DataPort Repository contains the data used primarily for generating Figure 1.
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The distributed generation, along with the deregulation of the Smart Grid, have created a great concern on Power Quality (PQ), as it has a direct impact on utilities and customers, as well as effects on the sinusoidal signal of the power line. The a priori unknown features of the distributed energy resources (DER) introduce non-linear behaviours in loads associated to a variety of PQ disturbances.
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This dataset contains Wi-Fi sensing data using Channel State Information (CSI) for various sleep disturbance parameters, from respiratory disturbances, to motion-based disturbances from posture shifts, leg restlessness and confusional arousals.The Wi-Fi CSI data was collected using the Wi-Fi module on the ESP32 Microcontroller units using the esp32-csi-tool.The Wi-Fi CSI respiratory disturbance data is accompanied by respiration belt data taken with the Wi-Fi measurements simultaneously using the Neulog NUL-236 respiration belt logger as ground truth.
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Voice Pre-processing and Quality Assessment Dataset (VPQAD), a scalable resource has been developed to validate various pre-processing techniques and improve voice signal quality in noisy environments. The dataset comprises voice recordings from 50 participants aged 18 to 40, captured in controlled real-life conditions using Audio Technica AT2020 and SHURE SM58 microphones. These high-quality recordings, made under diverse noise levels and settings, could be used for testing and developing voice enhancement algorithms.
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A set of non-stationary target vital sign signals measured by the FMCW radar, with data from a total of four testers, were tested for five sets of one minute each, with the testers approximately 1m away from the radar, in a non-stationary state. The radar-measured vital sign signals are stored in a .bin format file, the radar parameters are set in a Logfile.txt file, and the remaining two .txt files are the reference data collected with the respiratory and heartbeat sensors.
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This is the millimeter wave radar gesture recognition gesture dataset corresponding to paper <Gesture Recognition System Based on 24 GHz FMCW Radar Point Cloud with Limited Resources>. The file is stored in the. npy format of the numpy library in Python. It contains 4 gestures and 1 unrelated set of data, with a total of 5 categories. There are a total of 6000 pieces of data in each category, and each piece of raw data is packaged in a compressed package. The output screenshot of the script shows the details.
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The limited availability of Guitar notes datasets hinders the training of any artificial intelligence model in this field. TaptoTab dataset aims to fill this gap by providing a collection of notes recordings. This dataset is collected as part of an honours project at the Faculty of Computer and Information Sciences, Ain Shams University. The dataset is composed of audio data that has been self-collected, focusing on capturing a comprehensive range of guitar notes. The dataset consists of recordings of guitar notes played on each of the six strings, covering up to the 12th fret.
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**Abstract**
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In order to develop and analyse the performance of large-scale colored point cloud upsampling, we built a large-scale colored point cloud dataset for training and evaluating the upsampling network. This large-scale colored point cloud dataset consists of 121 original colored point clouds, 43 of which were scanned by us, while the other 78 were obtained from the SIAT-PCQD, Moving Picture Experts Group (MPEG) point cloud, and Greyc 3D colored mesh database. These point clouds cover six categories, including animals, plants, toys, sculptures, people and others.
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