Signal Processing
Introduced here is the Extended-Length Audio Dataset for Synthetic Voice Detection and Speaker Recognition (ELAD-SVDSR), a resource designed to advance research in synthetic voice (DeepFake) detection and automatic speaker recognition (ASR). It features around 45-minute audio recordings from 36 participants, each of whom read aloud different newspaper articles during controlled sessions, captured with five different high-quality microphones. Synthetic voices generated from 20 subjects of this dataset using open-source and commercial software are also included.
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The Clarkson University Affective Data Set (CUADS) is a multi-modal affective dataset designed to assist in machine learning model development for automated emotion recognition. CUADS provides electrocardiogram, photoplethysmogram, and galvanic skin response data from 38 participants, captured under controlled conditions using Shimmer3 ECG and GSR sensors. ECG, GSR and PPG signals were recorded while each participant viewed and rated 20 affective movie clips. CUADS also provides big five personality traits for each participant.
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This compressed file contains the original data (`.fig` files) of Figures 8 and 9 from the research on transforming noisy RF oscillators. These data are crucial for validating the phase noise reduction scheme of VCOs. They can provide in - depth insights into the impact of different LPF bandwidths and gains on phase noise, facilitating further research and verification in this field.
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This dataset was produced as part of the NANCY project (https://nancy-project.eu/), with the aim of using it in the fields of communication and
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GNSS-R Data for the paper: First Shipborne GNSS-R Ultra-long Observation Utilizing Oceangoing Freighter
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This dataset contains samples of the scattered field from a set of flat metallic objects. It includes several files corresponding to eight different measurement planes, where the distances between the plane containing the antennas and the plane containing the targets are 74.3913 cm, 66.4120 cm, 61.4173 cm, 56.4063 cm, 51.4131 cm, 46.3873 cm, 41.4244 cm, and 36.4289 cm. The measurement setup corresponds to a multistatic L-shaped configuration.
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现有的公开数据集往往存在数据量小，训练过程不充分，导致过拟合严重，泛化性能差的问题。针对这一问题，构建了雷达数据集 RadSet。在数据获取阶段，frequency modulated continuous wave (FMCW) radar system IWR1843 Boost manufactured by Texas Instruments (TI) was used. The RadSet dataset is collected by I+ Lab at Shandong University, covering a rectangular area 5 meters long in front of the radar and 4 meters wide, with the radar placed at a height of 120 cm above the ground. The volunteers execute the aforementioned activities at distances ranging between 1 to 5 meters from the radar.
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Repeated Route Naturalistic Driving Dataset (R2ND2) is a dual-perspective dataset for driver behavior analysis constituent of vehicular data collected using task-specific CAN decoding sensors using OBD port and external sensors, and (b) gaze-measurements collected using industry-standard multi-camera gaze calibration and collection system. Our experiment is designed to consider the variability associated with driving experience that depends on the time of day and provides valuable insights into the correlation of these additional metrics on driver behavior.
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In this study, MMW data are collected using a commercial handheld scanner (Vayyar's ECS2000), focusing on localized scans of the human body. The collected data are complex-valued (CV) high-resolution local 3D pseudo-images over a volume of 13×13×10 cm with spatial resolutions of 1.6 mm, 1.6 mm, and 4.3 mm in the x, y, and z directions, respectively. The compact, portable ECS2000 Vayyar's MMW scanner is built around a single RF board working in the frequency range of [60.4-69.9] GHz, housing transmitting and receiving antennas in a multiple-input multiple-output (MIMO) setup.
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The data set consists of exemplary results of the product of voltage noise power spectral density S(f) multiplied by frequency f and normalized to squared DC voltage U^2 recorded in the graphene back-gated Field Effect Transistor under UV light assistance (275 nm) in the selected ambient atmospheres (Figure 3) and the results of gas detection by SVM algorithm: 1) chloroform (Figure 5), 2) acetonitrile (Figure 6), and predicted gas concentrations using various number of frequency bins (Figure 7, Figure 8).
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