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Information
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Signal Processing Based on Machine Learning Techniques
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Signal Processing Based on Machine Learning Techniques

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: closed (30 June 2025) | Viewed by 7995

Special Issue Editors

Dr. Roberta Avanzato

E-Mail Website
Guest Editor
Department of Electrical, Electronic and Computer Engineering, University of Catania, 95125 Catania, Italy
Interests: signal processing; computer vision; convolutional neural networks; geolocation; drone communications
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Beritelli

E-Mail Website
Guest Editor
Department of Electrical, Electronic and Computer Engineering, University of Ca-tania, 95125 Catania, Italy
Interests: biomedical informatics; EEG; biometrics; signal theory; RMI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning (ML) has recently attracted a great deal of attention in the area of signal processing due to its intrinsic ability to analyze the signal in both the time and frequency domains.

Machine learning algorithms are used in many fields, especially in pattern recognition, signal classification, signal processing, computer vision, and biomedical technologies.

In recent years, research on signal processing has extended towards the use of artificial intelligence techniques and, in particular, towards recent machine learning techniques, which include the modern technologies of CNNs (convolutional neural networks) and DNNs (deep neural networks). The main advantages concern their greater accuracy in performance, in terms of robustness to signal degradation, and their lower computational complexity as a result of the possibility of processing data directly in the time domain without necessarily having to implement sets of features, which are typically obtained in the frequency domain. The application fields are numerous: speech recognition and identification, speech synthesis, classification of signals (image, speech, audio, and medical), recognition of emotions, automatic diagnosis, advanced methods and algorithms in smart sensors.

This Special Issue is devoted to reporting novel scientific ideas, approaches, results, and (prototype) solutions/applications on signal processing algorithms based on machine learning. Contributions are solicited in the wide spectrum of topics listed below.

  • Digital signal processing based on machine learning;
  • Signal processing algorithms and neural networks;
  • Artificial intelligence for multimedia signal processing;
  • Signal detection using machine learning;
  • CNNs and DNNs for signal classification and coding;
  • Application of CNNs to the diagnosis of biomedical signals;
  • Audio forensics analysis based on machine learning;
  • Video signal processing and CNNs;
  • Computer vision based on CNNs;
  • Pattern recognition and machine learning;
  • Rainfall estimation using a convolutional neural network.

Dr. Roberta Avanzato
Dr. Francesco Beritelli
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. Information 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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

13 pages, 1305 KiB  
Article
Fine-Tuning BirdNET for the Automatic Ecoacoustic Monitoring of Bird Species in the Italian Alpine Forests
by Giacomo Schiavo, Alessia Portaccio and Alberto Testolin
Information 2025, 16(8), 628; https://doi.org/10.3390/info16080628 - 23 Jul 2025
Viewed by 27
Abstract
The ongoing decline in global biodiversity constitutes a critical challenge for environmental science, necessitating the prompt development of effective monitoring frameworks and conservation protocols to safeguard the structure and function of natural ecosystems. Recent progress in ecoacoustic monitoring, supported by advances in artificial [...] Read more.
The ongoing decline in global biodiversity constitutes a critical challenge for environmental science, necessitating the prompt development of effective monitoring frameworks and conservation protocols to safeguard the structure and function of natural ecosystems. Recent progress in ecoacoustic monitoring, supported by advances in artificial intelligence, might finally offer scalable tools for systematic biodiversity assessment. In this study, we evaluate the performance of BirdNET, a state-of-the-art deep learning model for avian sound recognition, in the context of selected bird species characteristic of the Italian Alpine region. To this end, we assemble a comprehensive, manually annotated audio dataset targeting key regional species, and we investigate a variety of strategies for model adaptation, including fine-tuning with data augmentation techniques to enhance recognition under challenging recording conditions. As a baseline, we also develop and evaluate a simple Convolutional Neural Network (CNN) trained exclusively on our domain-specific dataset. Our findings indicate that BirdNET performance can be greatly improved by fine-tuning the pre-trained network with data collected within the specific regional soundscape, outperforming both the original BirdNET and the baseline CNN by a significant margin. These findings underscore the importance of environmental adaptation and data variability for the development of automated ecoacoustic monitoring devices while highlighting the potential of deep learning methods in supporting conservation efforts and informing soundscape management in protected areas. Full article
(This article belongs to the Special Issue Signal Processing Based on Machine Learning Techniques)
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9 pages, 1534 KiB  
Article
An Easily Customizable Approach for Automated Species-Specific Detection of Anuran Calls Using the European Green Toad as an Example
by Lukas Landler, Yurii V. Kornilev, Stephan Burgstaller, Janette Siebert, Maria Krall, Magdalena Spießberger, Daniel Dörler and Florian Heigl
Information 2024, 15(10), 610; https://doi.org/10.3390/info15100610 - 6 Oct 2024
Cited by 1 | Viewed by 1556
Abstract
Machine learning approaches for pattern recognition are increasingly popular. However, the underlying algorithms are often not open source, may require substantial data for model training, and are not geared toward specific tasks. We used open-source software to build a green toad breeding call [...] Read more.
Machine learning approaches for pattern recognition are increasingly popular. However, the underlying algorithms are often not open source, may require substantial data for model training, and are not geared toward specific tasks. We used open-source software to build a green toad breeding call detection algorithm that will aid in field data analysis. We provide instructions on how to reproduce our approach for other animal sounds and research questions. Our approach using 34 green toad call sequences and 166 audio files without green toad sounds had an accuracy of 0.99 when split into training (70%) and testing (30%) datasets. The final algorithm was applied to amphibian sounds newly collected by citizen scientists. Our function used three categories: “Green toad(s) detected”, “No green toad(s) detected”, and “Double check”. Ninety percent of files containing green toad calls were classified as “Green toad(s) detected”, and the remaining 10% as “Double check”. Eighty-nine percent of files not containing green toad calls were classified as “No green toad(s) detected”, and the remaining 11% as “Double check”. Hence, none of the files were classified in the wrong category. We conclude that it is feasible for researchers to build their own efficient pattern recognition algorithm. Full article
(This article belongs to the Special Issue Signal Processing Based on Machine Learning Techniques)
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12 pages, 1668 KiB  
Article
Bridging Artificial Intelligence and Neurological Signals (BRAINS): A Novel Framework for Electroencephalogram-Based Image Generation
by Mateo Sokač, Leo Mršić, Mislav Balković and Maja Brkljačić
Information 2024, 15(7), 405; https://doi.org/10.3390/info15070405 - 12 Jul 2024
Cited by 2 | Viewed by 5098
Abstract
Recent advancements in cognitive neuroscience, particularly in electroencephalogram (EEG) signal processing, image generation, and brain–computer interfaces (BCIs), have opened up new avenues for research. This study introduces a novel framework, Bridging Artificial Intelligence and Neurological Signals (BRAINS), which leverages the power of artificial [...] Read more.
Recent advancements in cognitive neuroscience, particularly in electroencephalogram (EEG) signal processing, image generation, and brain–computer interfaces (BCIs), have opened up new avenues for research. This study introduces a novel framework, Bridging Artificial Intelligence and Neurological Signals (BRAINS), which leverages the power of artificial intelligence (AI) to extract meaningful information from EEG signals and generate images. The BRAINS framework addresses the limitations of traditional EEG analysis techniques, which struggle with nonstationary signals, spectral estimation, and noise sensitivity. Instead, BRAINS employs Long Short-Term Memory (LSTM) networks and contrastive learning, which effectively handle time-series EEG data and recognize intrinsic connections and patterns. The study utilizes the MNIST dataset of handwritten digits as stimuli in EEG experiments, allowing for diverse yet controlled stimuli. The data collected are then processed through an LSTM-based network, employing contrastive learning and extracting complex features from EEG data. These features are fed into an image generator model, producing images as close to the original stimuli as possible. This study demonstrates the potential of integrating AI and EEG technology, offering promising implications for the future of brain–computer interfaces. Full article
(This article belongs to the Special Issue Signal Processing Based on Machine Learning Techniques)
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