LE QUY DON
Technical University
VietnameseClear Cookie - decide language by browser settings

An Integrated Toolbox of Time-Frequency Techniques for Preprocessing in AI Networks

Nguyen, T.P. and Nguyen, T.T. and Nguyen, V.M. and Le, H.D. (2024) An Integrated Toolbox of Time-Frequency Techniques for Preprocessing in AI Networks. In: International Conference on Intelligent Systems and Networks, ICISN 2024, 22 March 2024 Through 23 March 2024, Hanoi.

Full text not available from this repository. (Upload)

Abstract

Artificial neural networks have recently been widely used in classifying signals with high accuracy and without manual feature extraction procedures. Additionally, simultaneously presenting signals in both time and frequency domains, time-frequency images have been frequently utilized as inputs to train a convolutional neural network. Some typical time-frequency analysis techniques, i.e., the short-time Fourier transform, continuous wavelet transform, Wigner-Ville distribution, and the Hilbert-Huang transform, have been approved to solve complex classification problems in convolutional neural network-based architectures. However, those techniques were considered separately, and one network commonly uses only one method for some types of signals. There is no tool to evaluate which time-frequency techniques are the best for which signals. This paper integrates a time-frequency analysis toolbox to deal with that problem. From then on, it will be an essential preprocessing step in convolutional neural network-based architectures to enhance classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Item Type: Conference or Workshop Item (Paper)
Divisions: Institutes > Institute of Simulation Technology
Identification Number: 10.1007/978-981-97-5504-2₁₄
Uncontrolled Keywords: Convolutional neural networks; Fourier transforms; Frequency domain analysis; Hilbert-Huang transform; Image enhancement; Linear transformations; Wavelet analysis; Wavelet transforms, Convolutional neural network; Convolutional neuron network; High-accuracy; Network-based architectures; Neural-networks; Neuron networks; Signal classification; Signal preprocessing; Time-frequency Analysis; Time-frequency techniques, Wigner-Ville distribution
Additional Information: Conference of International Conference on Intelligent Systems and Networks, ICISN 2024 ; Conference Date: 22 March 2024 Through 23 March 2024; Conference Code:318189
URI: http://eprints.lqdtu.edu.vn/id/eprint/11371

Actions (login required)

View Item
View Item