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 paper includes a variety of media location data generated in the paper.According to the wideband near-field signal propagation model, sample data were generated based on four layers of media with known media types. The main differences between simulated data and real data are errors due to electromagnetic dispersion, multipath and noise, which are not currently taken into account because of different electromagnetic characteristics in inhomogeneous media. The setting of condition variables in the experiment was shown in Table I.

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Six publicly available datasets are applied to Speech/Music Classification (SMC), Music Genre Classification (MGC), and Environmental Sound Classification (ESC), respectively. The utilized datasets include: 1) For SMC tasks, we employed the GTZAN-SMC and MUSAN datasets. 2) In ESC, the commonly employed ESC-10 and US8K datasets were included. 3) Classic GTZAN and Homburg datasets for MGC. 

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Moroccan Dialect Emotion Recognition Dataset is a collection of voice records of people speaking Moroccan dialect in 5 states of emotion: Neutral, Happy, Sad, Angry and Fearful. The dataset has been collected in different Moroccan cities in 2024. Each recorder has 5 records per emotion class. The dataset contains 2000 record. The records are saved in .wav format, which is useful for signal processing with python libraries. The dataset is used for signal processing and emotion recognition using deep Learning models.

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Measurement campaign was performed across the entire growing season of 2023. Seven Arduino+LoRaWAN sensors were measuring soil moisture, temperature and pH, as well as solar irradiance in the radius of about 35 km around Gdansk, Poland. Raw data were being collected with a nomadic gateway aboard an UAV and transferred to the cloud for analysis (package part A). Based on the physics informed analysis of the underlying measurement processes anomalies were classified and parametrized.

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In our work, we propose an innovative system to accurately infer and track occluded target locations using mmWave beat frequency signals. Our approach combines a classic direction-finding method with advanced deep learning techniques, specifically a convolutional neural network (CNN), to enhance detection capabilities. The dataset includes raw beat frequency signal data from the TI IWR6843ISK rev B with TI mmWAVEICBOOST and the TI DCA1000EVM capture board. Corresponding ground truth data (target position) from the Realsense L515 RGB-D camera is also provided.

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This dataset is utilized for the research of blind identification of CPM signal modulation order. The signal parameters in the dataset range as follows: modulation index from 0.125 to 1, modulation order of 2, 4, 8, pulse types including REC, RC, SRC, TFM, and GMSK, and correlation lengths of 1 to 8. The signals are oversampled by a factor of 10, transmitted through an additive white Gaussian noise (AWGN) channel, and the signal-to-noise ratio (SNR) ranges from 0 to 30dB. 

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This dataset contains the measurement in an ultrawide band (UWB) system in the 6.5 GHz band, considering the presence of the human body as the only obstacle. There are measurements in line-of-sight condition to compare the results of the analysis performed. The measurements are part of our research on the adverse effects of the body shadowing in pedestrian localization systems. The measurements were obtained in three distinct scenarios.

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