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Signals, Volume 5, Issue 4 (December 2024) – 7 articles

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20 pages, 8075 KiB  
Article
Comparative Analysis of Statistical, Time–Frequency, and SVM Techniques for Change Detection in Nonlinear Biomedical Signals
by Tahmineh Azizi
Signals 2024, 5(4), 736-755; https://doi.org/10.3390/signals5040041 (registering DOI) - 7 Nov 2024
Viewed by 122
Abstract
Change detection in biomedical signals is crucial for understanding physiological processes and diagnosing medical conditions. This study evaluates various change detection methods, focusing on synthetic signals that mimic real-world scenarios. We examine the following three methods: classical statistical techniques (thresholding based on mean [...] Read more.
Change detection in biomedical signals is crucial for understanding physiological processes and diagnosing medical conditions. This study evaluates various change detection methods, focusing on synthetic signals that mimic real-world scenarios. We examine the following three methods: classical statistical techniques (thresholding based on mean and standard deviation), Support Vector Machine (SVM) classification, and time–frequency analysis using Continuous Wavelet Transform (CWT). Each method’s performance is assessed using synthetic signals, including nonlinear signals and those with simulated anomalies. We calculated the F1-score to quantify performance, providing a balanced measure of precision and recall. Results showed that SVM classification outperformed both classical techniques and CWT analysis, achieving a higher F1-score in detecting changes. While all methods struggled with synthetic nonlinear signals, classical techniques and SVM successfully detected changes in signals with simulated anomalies, whereas CWT had difficulty with both types of signals. These findings underscore the importance of selecting appropriate change detection methods based on signal characteristics. Future research should explore advanced machine learning and signal processing techniques to improve detection accuracy in biomedical applications. Full article
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15 pages, 13201 KiB  
Article
Quantifying Shape and Texture Biases for Enhancing Transfer Learning in Convolutional Neural Networks
by Akinori Iwata and Masahiro Okuda
Signals 2024, 5(4), 721-735; https://doi.org/10.3390/signals5040040 - 4 Nov 2024
Viewed by 331
Abstract
Neural networks have inductive biases owing to the assumptions associated with the selected learning algorithm, datasets, and network structure. Specifically, convolutional neural networks (CNNs) are known for their tendency to exhibit textural biases. This bias is closely related to image classification accuracy. Aligning [...] Read more.
Neural networks have inductive biases owing to the assumptions associated with the selected learning algorithm, datasets, and network structure. Specifically, convolutional neural networks (CNNs) are known for their tendency to exhibit textural biases. This bias is closely related to image classification accuracy. Aligning the model’s bias with the dataset’s bias can significantly enhance performance in transfer learning, leading to more efficient learning. This study aims to quantitatively demonstrate that increasing shape bias within the network by varying kernel sizes and dilation rates improves accuracy on shape-dominant data and enables efficient learning with less data. Furthermore, we propose a novel method for quantitatively evaluating the balance between texture bias and shape bias. This method enables efficient learning by aligning the biases of the transfer learning dataset with those of the model. Systematically adjusting these biases allows CNNs to better fit data with specific biases. Compared to the original model, an accuracy improvement of up to 9.9% was observed. Our findings underscore the critical role of bias adjustment in CNN design, contributing to developing more efficient and effective image classification models. Full article
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16 pages, 2500 KiB  
Article
Curved Text Line Rectification via Bresenham’s Algorithm and Generalized Additive Models
by Thomas Stogiannopoulos and Ilias Theodorakopoulos
Signals 2024, 5(4), 705-720; https://doi.org/10.3390/signals5040039 - 24 Oct 2024
Viewed by 494
Abstract
This paper presents a methodology for rectifying curved text lines, a crucial process in optical character recognition (OCR) and computer vision. Utilizing generalized additive models (GAMs), the proposed method accurately estimates text curvature and rectifies it into a straight format for improved text [...] Read more.
This paper presents a methodology for rectifying curved text lines, a crucial process in optical character recognition (OCR) and computer vision. Utilizing generalized additive models (GAMs), the proposed method accurately estimates text curvature and rectifies it into a straight format for improved text recognition. The process includes image binarization techniques like Otsu’s thresholding, morphological operations, curve estimation, and the Bresenham line drawing algorithm. The results show significant improvements in OCR accuracy among different challenging distortion scenarios. The implementation, written in Python, demonstrates the potential for enhancing text alignment and rectification in scanned text line images utilizing a flexible, robust, and customizable framework. Full article
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15 pages, 2242 KiB  
Article
Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data
by Nishanth Anandanadarajah, Amlan Talukder, Deryck Yeung, Yuanyuan Li, David M. Umbach, Zheng Fan and Leping Li
Signals 2024, 5(4), 690-704; https://doi.org/10.3390/signals5040038 - 22 Oct 2024
Viewed by 462
Abstract
Polysomnography (PSG) measures brain activity during sleep via electroencephalography (EEG) using six leads. Artifacts caused by movement or loose leads distort EEG measurements. We developed a method to automatically identify such artifacts in a PSG EEG trace. After preprocessing, we extracted power levels [...] Read more.
Polysomnography (PSG) measures brain activity during sleep via electroencephalography (EEG) using six leads. Artifacts caused by movement or loose leads distort EEG measurements. We developed a method to automatically identify such artifacts in a PSG EEG trace. After preprocessing, we extracted power levels at frequencies of 0.5–32.5 Hz with multitaper spectral analysis using 4 s windows with 3 s overlap. For each resulting 1 s segment, we computed segment-specific correlations between power levels for all pairs of leads. We then averaged all pairwise correlation coefficients involving each lead, creating a time series of segment-specific average correlations for each lead. Our algorithm scans each averaged time series separately for “bad” segments using a local moving window. In a second pass, any segment whose averaged correlation is less than a global threshold among all remaining good segments is declared an outlier. We mark all segments between two outlier segments fewer than 300 s apart as artifact regions. This process is repeated, removing a channel with excessive outliers in each iteration. We compared artifact regions discovered by our algorithm to expert-assessed ground truth, achieving sensitivity and specificity of 80% and 91%, respectively. Our algorithm is an open-source tool, either as a Python package or a Docker. Full article
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31 pages, 1004 KiB  
Article
Daily Streamflow Forecasting Using AutoML and Remote-Sensing-Estimated Rainfall Datasets in the Amazon Biomes
by Matteo Bodini
Signals 2024, 5(4), 659-689; https://doi.org/10.3390/signals5040037 - 10 Oct 2024
Viewed by 664
Abstract
Reliable streamflow forecasting is crucial for several tasks related to water-resource management, including planning reservoir operations, power generation via Hydroelectric Power Plants (HPPs), and flood mitigation, thus resulting in relevant social implications. The present study is focused on the application of Automated Machine-Learning [...] Read more.
Reliable streamflow forecasting is crucial for several tasks related to water-resource management, including planning reservoir operations, power generation via Hydroelectric Power Plants (HPPs), and flood mitigation, thus resulting in relevant social implications. The present study is focused on the application of Automated Machine-Learning (AutoML) models to forecast daily streamflow in the area of the upper Teles Pires River basin, located in the region of the Amazon biomes. The latter area is characterized by extensive water-resource utilization, mostly for power generation through HPPs, and it has a limited hydrological data-monitoring network. Five different AutoML models were employed to forecast the streamflow daily, i.e., auto-sklearn, Tree-based Pipeline Optimization Tool (TPOT), H2O AutoML, AutoKeras, and MLBox. The AutoML input features were set as the time-lagged streamflow and average rainfall data sourced from four rain gauge stations and one streamflow gauge station. To overcome the lack of training data, in addition to the previous features, products estimated via remote sensing were leveraged as training data, including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now. The selected AutoML models proved their effectiveness in forecasting the streamflow in the considered basin. In particular, the reliability of streamflow predictions was high both in the case when training data came from rain and streamflow gauge stations and when training data were collected by the four previously mentioned estimated remote-sensing products. Moreover, the selected AutoML models showed promising results in forecasting the streamflow up to a three-day horizon, relying on the two available kinds of input features. As a final result, the present research underscores the potential of employing AutoML models for reliable streamflow forecasting, which can significantly advance water-resource planning and management within the studied geographical area. Full article
(This article belongs to the Special Issue Rainfall Estimation Using Signals)
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17 pages, 1252 KiB  
Article
Interpretability of Methods for Switch Point Detection in Electronic Dance Music
by Mickaël Zehren, Marco Alunno and Paolo Bientinesi
Signals 2024, 5(4), 642-658; https://doi.org/10.3390/signals5040036 - 8 Oct 2024
Viewed by 590
Abstract
Switch points are a specific kind of cue point that DJs carefully look for when mixing music tracks. As the name says, a switch point is the point in time where the current track in a DJ mix is replaced by the upcoming [...] Read more.
Switch points are a specific kind of cue point that DJs carefully look for when mixing music tracks. As the name says, a switch point is the point in time where the current track in a DJ mix is replaced by the upcoming track. Being able to identify these positions is a first step toward the interpretation and the emulation of DJ mixes. With the aim of automatically detecting switch points, we evaluate one experience-driven and several statistics-driven methods. By comparing the decision process of each method, contrasted by their performance, we deduce the characteristics linked to switch points. Specifically, we identify the most impactful features for their detection, namely, the novelty in the signal energy, the timbre, the number of drum onsets, and the harmony. Furthermore, we expose multiple interactions among these features. Full article
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9 pages, 2212 KiB  
Article
Adaptive Filtering for Multi-Track Audio Based on Time–Frequency Masking Detection
by Wenhan Zhao and Fernando Pérez-Cota
Signals 2024, 5(4), 633-641; https://doi.org/10.3390/signals5040035 - 2 Oct 2024
Viewed by 487
Abstract
There is a growing need to facilitate the production of recorded music as independent musicians are now key in preserving the broader cultural roles of music. A critical component of the production of music is multitrack mixing, a time-consuming task aimed at, among [...] Read more.
There is a growing need to facilitate the production of recorded music as independent musicians are now key in preserving the broader cultural roles of music. A critical component of the production of music is multitrack mixing, a time-consuming task aimed at, among other things, reducing spectral masking and enhancing clarity. Traditionally, this is achieved by skilled mixing engineers relying on their judgment. In this work, we present an adaptive filtering method based on a novel masking detection scheme capable of identifying masking contributions, including temporal interchangeability between the masker and maskee. This information is then systematically used to design and apply filters. We implement our methods on multitrack music to improve the quality of the raw mix. Full article
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