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Electronics
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Machine Learning for Signals Processing
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Machine Learning for Signals Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 5174

Special Issue Editors

Dr. Michał Jasiński

E-Mail Website
Guest Editor
1. Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
2. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 708-00 Ostrava, Czech Republic
Interests: power quality; electrical power engineering; renewable energy technologies; machine learning; clean energy; multi-energy systems; data mining; sustainable development
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zbigniew Leonowicz

E-Mail Website
Guest Editor
1. Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
2. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 708-00 Ostrava, Czech Republic
Interests: signal analysis; advanced signal processing methods; renewable energy; ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The utilization of scientific principles and technology has enabled the creation of intelligent systems that learn and improve from experience, which is a fundamental concept of machine learning. Machine learning algorithms can be applied to various fields, including image processing, natural language processing, speech recognition, and signal processing. Signal processing is a crucial component in many modern systems and applications, including telecommunications, image and video processing, and control systems. The application of machine learning to signal processing has led to the development of novel methods for feature extraction, classification, and prediction, among others. This Special Issue aims to provide a platform for the publication of original research articles and reviews that focus on the integration of machine learning and signal processing for the development of intelligent systems that can operate in complex and dynamic environments. The contributions will provide valuable insights into the latest research trends, techniques, and applications of machine learning in signal processing, including, but not limited to:

  • Developing novel deep learning architectures for signal processing tasks, such as time-series forecasting, speech recognition, and image processing.
  • Investigating the integration of multiple modalities of data, such as audio and visual data, for improved signal processing performance.
  • Developing techniques for feature extraction and dimensionality reduction in large datasets, including unsupervised learning methods.
  • Investigating the use of transfer learning techniques for signal processing applications, allowing for the transfer of knowledge learned from one task to another related task.
  • Evaluating the performance of different machine learning algorithms for signal processing, such as decision trees, random forests, support vector machines, and deep neural networks.
  • Developing new signal processing applications using machine learning techniques, such as anomaly detection, signal denoising, and signal classification.
  • Investigating the use of machine learning for real-time signal processing applications, including embedded systems and edge computing platforms.

Dr. Michał Jasiński
Prof. Dr. Zbigniew Leonowicz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • signal processing
  • deep learning
  • neural networks

Published Papers (3 papers)

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Research

23 pages, 1017 KiB  
Article
A Deep Learning Approach for Speech Emotion Recognition Optimization Using Meta-Learning
by Lara Toledo Cordeiro Ottoni, André Luiz Carvalho Ottoni and Jés de Jesus Fiais Cerqueira
Electronics 2023, 12(23), 4859; https://doi.org/10.3390/electronics12234859 - 01 Dec 2023
Cited by 1 | Viewed by 1705
Abstract
Speech emotion recognition (SER) is widely applicable today, benefiting areas such as entertainment, robotics, and healthcare. This emotional understanding enhances user-machine interaction, making systems more responsive and providing more natural experiences. In robotics, SER is useful in home assistance devices, eldercare, and special [...] Read more.
Speech emotion recognition (SER) is widely applicable today, benefiting areas such as entertainment, robotics, and healthcare. This emotional understanding enhances user-machine interaction, making systems more responsive and providing more natural experiences. In robotics, SER is useful in home assistance devices, eldercare, and special education, facilitating effective communication. Additionally, in healthcare settings, it can monitor patients’ emotional well-being. However, achieving high levels of accuracy is challenging and complicated by the need to select the best combination of machine learning algorithms, hyperparameters, datasets, data augmentation, and feature extraction methods. Therefore, this study aims to develop a deep learning approach for optimal SER configurations. It delves into the domains of optimizer settings, learning rates, data augmentation techniques, feature extraction methods, and neural architectures for the RAVDESS, TESS, SAVEE, and R+T+S (RAVDESS+TESS+SAVEE) datasets. After finding the best SER configurations, meta-learning is carried out, transferring the best configurations to two additional datasets, CREMA-D and R+T+S+C (RAVDESS+TESS+SAVEE+CREMA-D). The developed approach proved effective in finding the best configurations, achieving an accuracy of 97.01% for RAVDESS, 100% for TESS, 90.62% for SAVEE, and 97.37% for R+T+S. Furthermore, using meta-learning, the CREMA-D and R+T+S+C datasets achieved accuracies of 83.28% and 90.94%, respectively. Full article
(This article belongs to the Special Issue Machine Learning for Signals Processing)
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20 pages, 6614 KiB  
Article
Human Action Recognition Using Key-Frame Attention-Based LSTM Networks
by Changxuan Yang, Feng Mei, Tuo Zang, Jianfeng Tu, Nan Jiang and Lingfeng Liu
Electronics 2023, 12(12), 2622; https://doi.org/10.3390/electronics12122622 - 10 Jun 2023
Viewed by 1447
Abstract
Human action recognition is a classical problem in computer vision and machine learning, and the task of effectively and efficiently recognising human actions is a concern for researchers. In this paper, we propose a key-frame-based approach to human action recognition. First, we designed [...] Read more.
Human action recognition is a classical problem in computer vision and machine learning, and the task of effectively and efficiently recognising human actions is a concern for researchers. In this paper, we propose a key-frame-based approach to human action recognition. First, we designed a key-frame attention-based LSTM network (KF-LSTM) using the attention mechanism, which can be combined with LSTM to effectively recognise human action sequences by assigning different weight scale values to give more attention to key frames. In addition, we designed a new key-frame extraction method by combining an automatic segmentation model based on the autoregressive moving average (ARMA) algorithm and the K-means clustering algorithm. This method effectively avoids the possibility of inter-frame confusion in the temporal sequence of key frames of different actions and ensures that the subsequent human action recognition task proceeds smoothly. The dataset used in the experiments was acquired with an IMU sensor-based motion capture device, and we separately extracted the motion features of each joint using a manual method and then performed collective inference. Full article
(This article belongs to the Special Issue Machine Learning for Signals Processing)
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18 pages, 6990 KiB  
Article
Cable Broken Wire Signal Recognition Based on Convolutional Neural Network
by Wanxu Zhu, Runzi Liu, Peng Jiang and Jiazhu Huang
Electronics 2023, 12(9), 2138; https://doi.org/10.3390/electronics12092138 - 07 May 2023
Viewed by 1547
Abstract
Due to the long-term exposure of bridge ties to complex environments, their internal steel wires are prone to corrosion damage, which may lead to tie breakage accidents if not detected in time. Although existing advanced monitoring methods can be used to obtain the [...] Read more.
Due to the long-term exposure of bridge ties to complex environments, their internal steel wires are prone to corrosion damage, which may lead to tie breakage accidents if not detected in time. Although existing advanced monitoring methods can be used to obtain the broken wire signal, they either still need the damage to be identified manually or are limited by the training data set. To address this problem, a model combination consisting of a classification model and three regression models was built based on convolutional neural networks to predict the location of broken wires after first classifying them based on features. We developed software-containing data set generation and model performance testing functions, in which we used original algorithms to expand the broken wire data set for training based on the measured data obtained from FBG sensors with a sampling frequency of 100 Hz, thus generating more than 22,000 types of data. The performance test results showed that the model combination successfully detected 11,972 broken wires among 12,000 test data points generated by the algorithm, with a recognition success rate of 99.77% and an average time of 0.0076 s between the predicted location and the actual broken wire location, with an error rate of 0.38%. In the test of 118 real broken wires, the model detected all the abnormalities, and the average time between the predicted location and the actual broken wire location was 0.0695 s, with an error of 3.48%. This verified the feasibility of using artificial intelligence to accurately identify broken wire signals and can provide a reference for the subsequent intelligent identification of tie abnormalities. Full article
(This article belongs to the Special Issue Machine Learning for Signals Processing)
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