Special Issue "Adaptive Signal Processing and Machine Learning Using Entropy and Information Theory"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: 31 May 2021.

Special Issue Editors

Prof. Dr. Tokunbo Ogunfunmi
Guest Editor
School of Engineering, Santa Clara University, 500 El Camino Real, Santa Clara, CA 95053, USA
Interests: adaptive filters; signal processing; speech; video; communications
Prof. Dr. David Luengo
Guest Editor
Department of Audiovisual and Communications Engineering, Technical University of Madrid, C/Nikola Tesla s/n, 28031 Madrid, Spain
Interests: biomedical signal processing; machine learning; adaptive Monte Carlo methods; Bayesian computation; statistical inference
Prof. Dr. Nithin V George
Guest Editor
Indian Institute of Technology, Gandhinagar, India
Interests: active noise control; adaptive signal processing; assistive listening devices; psychoacoustics
Prof. Dr. Danilo Comminiello
Guest Editor
Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana n°18, 00184 Rome, Italy
Interests: adaptive filters; machine learning; audio and acoustic signal processing

Special Issue Information

Dear Colleagues,

Adaptive signal processing, machine learning, and deep learning which rely on the paradigm of learning from data have become indispensable tools for extracting information, making decisions, and interacting with our environment. The information extraction process is a very critical step in this process. Many of the algorithms deployed for information extraction have largely been based on using the popular mean square error (MSE) criterion. They leverage the significant information contained in the data. The more accurate the process of extracting useful information from the data, the more precise and efficient the learning and signal processing will be.

However, traditional mean square error (MSE) is not the optimal cost measure to use, when the error distribution is non-Gaussian, such as in supervised learning. In such cases, information theoretic learning (ITL)-based cost measures can provide better nonlinear models in a range of problems from system identification and regression to classification. Information theoretic learning (ITL) has initially been applied for such supervised learning applications.

Entropy and information theory have always represented useful tools to deal with information and the amount of information contained in a random variable. Information theory mainly relies on the basic intuition that learning that an unlikely event has occurred is more informative than learning that a likely event has occurred. Entropy gives a measure of the amount of information in an event drawn from a distribution. For this reason, they have been widely used in adaptive signal processing and machine learning to improve performance by designing and optimizing effective and specific models that fit the data even in noisy and adverse scenario conditions.

The presence of strong disturbances in the error signal can severely deteriorate convergence behavior of adaptive filters and in some cases cause the learning algorithms to diverge. Information theoretic learning (ITL) approaches have recently emerged as an effective solution to handle such scenarios.

Examples of several widely adopted measures include mutual information, cross-entropy, minimum error entropy (MEE) criterion, maximum correntropy criterion (MCC) and Kullback–Leibler divergence, among others. Moreover, a wide class of interesting tasks of adaptive signal processing, machine learning, and deep learning take advantage of entropy and information theory, including exploratory data analysis, feature and model selection, sampling and subset extraction, optimizing learning algorithms, clustering sensitivity analysis, representation learning, and data generation.

This Special Issue aims at providing recent developments in the areas of adaptive signal processing, machine learning, and deep learning using information theory and entropy to improve performance in widespread and popular problems and also to provide effective solutions to emerging problems.

The scope of the Special Issue includes theoretical and applications papers pertaining to all problems involving learning from data.

Prof. Dr. Tokunbo Ogunfunmi
Dr. David Luengo
Dr. Nithin V George
Dr. Danilo Comminiello
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 papers will be 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. Entropy is an international peer-reviewed open access monthly 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 1800 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.


  • Adaptive signal processing, adaptive filters
  • Machine listening, deep learning
  • Information theoretic learning
  • Generalized maximum correntropy criterion (GMCC)
  • Maximum correntropy criterion (MCC), cyclic correntropy
  • Nonlinear adaptive filters
  • Robust signal processing, robust learning
  • Impulsive noise
  • Model selection and feature extraction
  • Bayesian learning and representation learning

Published Papers (1 paper)

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Open AccessArticle
A Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy and Its Application to Multivariate Signal of Rotating Machinery
Entropy 2021, 23(1), 128; https://doi.org/10.3390/e23010128 - 19 Jan 2021
Viewed by 406
In the fault monitoring of rotating machinery, the vibration signal of the bearing and gear in a complex operating environment has poor stationarity and high noise. How to accurately and efficiently identify various fault categories is a major challenge in rotary fault diagnosis. [...] Read more.
In the fault monitoring of rotating machinery, the vibration signal of the bearing and gear in a complex operating environment has poor stationarity and high noise. How to accurately and efficiently identify various fault categories is a major challenge in rotary fault diagnosis. Most of the existing methods only analyze the single channel vibration signal and do not comprehensively consider the multi-channel vibration signal. Therefore, this paper presents Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy (RCMMFDE), a method which extracts the recognition information of multi-channel signals with different scale factors, and the refined composite analysis ensures the recognition stability. The simulation results show that this method has the characteristics of low sensitivity to signal length and strong anti-noise ability. At the same time, combined with Joint Mutual Information Maximisation (JMIM) and support vector machine (SVM), RCMMFDE-JMIM-SVM fault diagnosis method has been proposed. This method uses RCMMFDE to extract the state characteristics of the multiple vibration signals of the rotary machine, and then uses the JMIM method to extract the sensitive characteristics. Finally, different states of the rotary machine are classified by SVM. The validity of the method is verified by the composite gear fault data set and bearing fault data set. The diagnostic accuracy of the method is 99.25% and 100.00%. The experimental results show that RCMMFDE-JMIM-SVM can effectively recognize multiple signals. Full article
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