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Algorithms
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Machine Learning Algorithms for Signal Processing
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Machine Learning Algorithms for Signal Processing

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1676

Special Issue Editor

Dr. Fred Lacy

E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, Southern University and A&M College, Baton Rouge, LA 70807, USA
Interests: electronics based sensors; electronic transport in conductors; biosensors and bioelectrodes; thin films

Special Issue Information

Dear Colleagues,

Sensors and cameras are designed to provide data or information to the user. Nevertheless, analysis must be performed on the data to reach any conclusions. However, because these data are often corrupted by noise and multivariate (i.e., multiple factors influence the data), sound conclusions are difficult to obtain. Thus, these data usually need to undergo some type of signal processing to allow us to reach a reliable conclusion. Moreover, as the complexity and volume of data continue to increase, traditional signal processing methods will not be adequate.  Therefore, innovative solutions for analyzing complex data are needed to advance the fields of signal processing and data analysis. 

Machine learning (ML) algorithms are becoming increasingly popular and are being utilized for tasks such as classifying information, processing speech or language, facial recognition, and predicting trends in data. These techniques offer groundbreaking solutions for signal processing because they uncover patterns in data, allowing for noise reduction and anomaly detection. By utilizing these algorithms independently or integrating them into signal processing software, researchers and engineers can improve the accuracy of data analysis and optimize performance in communication systems, medical imaging, and audio processing.

This Special Issue of the journal Algorithms will present research on machine learning methods with uses in signal processing, serving as an invaluable resource compiling cutting-edge research with real-world applications and bridging the important fields of data analytics and signal processing.

Dr. Fred Lacy
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Algorithms 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.

Keywords

  • machine learning
  • signal processing
  • signal analysis
  • image processing
  • speech processing
  • data analytics
  • noise reduction
  • filtering
  • pattern recognition
  • anomaly detection

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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

25 pages, 1614 KB  
Article
Deep Multi-Modal Kernel Map Network for Music Genre Classification
by Qun Wang and Mingyuan Jiu
Algorithms 2026, 19(6), 467; https://doi.org/10.3390/a19060467 (registering DOI) - 8 Jun 2026
Abstract
Music genre classification is an important task in the music information retrieval community that aims to categorize music samples by genre; it can help to retrieve music more easily and efficiently from huge digital music resources. There is an extensive literature on music [...] Read more.
Music genre classification is an important task in the music information retrieval community that aims to categorize music samples by genre; it can help to retrieve music more easily and efficiently from huge digital music resources. There is an extensive literature on music genre classification, and in this study, we solve the problem using multi-modal information, especially based on music audio and text. We propose a deep multi-modal kernel map network that learns discriminative features in a high-dimensional kernel Hilbert space by fusing the multi-modal features. For the music audio, Mel Frequency Cepstral Coefficients (MFCCs) are extracted and a pre-trained ResNet is applied to extract the features. For the texts, the pre-trained RoBERTa model is applied to extract the semantic symbolic features. In the network’s input layer, we calculate four exact/approximated elementary kernel maps from the audio and text features; in the intermediate and final layer, we progressively compute the nonlinear combination of preceding kernel maps of different modalities, followed by a fully connected layer for classification. The network can be trained end-to-end to jointly learn the combination weights between modalities and classifier parameters. We apply the proposed network on the public GTZAN dataset, multi-modal piano genre dataset, and 4MuLA dataset, and the experimental results validate the effectiveness of the proposed deep multi-modal kernel map network for music genre classification. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Signal Processing)
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26 pages, 3379 KB  
Article
A Hybrid Algorithm for Fault Diagnosis in Nonlinear UAV Systems Using Conditional LSTM Autoencoders
by Yair González-Baldizón, José-Armando Fragoso-Mandujano, Norberto Urbina-Brito, Eduardo Chandomí-Castellanos, Jorge-Iván Bermúdez-Rodríguez, Esvan-Jesús Pérez-Pérez and Julio-Alberto Guzmán-Rabasa
Algorithms 2026, 19(6), 463; https://doi.org/10.3390/a19060463 - 7 Jun 2026
Viewed by 95
Abstract
This paper presents a hybrid algorithmic framework for fault detection and isolation (FDI) in nonlinear quadrotor unmanned aerial vehicle (UAV) systems operating under closed-loop conditions. The proposed method integrates a Linear Quadratic Control (LQC) strategy, synthesized through Linear Matrix Inequalities (LMIs), with a [...] Read more.
This paper presents a hybrid algorithmic framework for fault detection and isolation (FDI) in nonlinear quadrotor unmanned aerial vehicle (UAV) systems operating under closed-loop conditions. The proposed method integrates a Linear Quadratic Control (LQC) strategy, synthesized through Linear Matrix Inequalities (LMIs), with a Conditional Long Short-Term Memory Autoencoder (CLSTM-AE) and an adaptive residual-based decision mechanism. The LQC scheme provides robust trajectory tracking through regional pole-placement constraints, while the CLSTM-AE learns the nominal closed-loop input–output temporal behavior of the UAV using only fault-free data. In contrast to conventional symmetric autoencoder-based detectors, the proposed CLSTM-AE uses the control inputs together with the available attitude estimates, represented by the Euler angles yaw, pitch, and roll, as conditioning information, while reconstructing only the monitored attitude outputs. This asymmetric structure allows the residuals to capture inconsistencies between the commanded control effort and the observed attitude response, which is particularly relevant in closed-loop nonlinear systems where feedback compensation may attenuate fault signatures. Deviations from nominal behavior are detected through reconstruction residuals computed using a smoothed Mean Squared Error (MSE) criterion and evaluated against an adaptive 3σ threshold. The framework is validated in three-dimensional flight simulations considering abrupt, transient, and incipient actuator fault scenarios. The obtained results show that the proposed approach outperforms representative conventional machine-learning methods, achieving an average accuracy of 98.2%, an average recall of 97.8%, and an average false positive rate of 1.4%. These results suggest that the proposed hybrid algorithm provides an effective and interpretable solution for closed-loop fault diagnosis in nonlinear UAV systems under measurement noise and system variability. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Signal Processing)
27 pages, 11057 KB  
Article
A Variable-Speed and Multi-Condition Bearing Fault Diagnosis Method Based on Adaptive Signal Decomposition and Deep Feature Fusion
by Ting Li, Mingyang Yu, Tianyi Ma, Yanping Du and Shuihai Dou
Algorithms 2025, 18(12), 753; https://doi.org/10.3390/a18120753 - 28 Nov 2025
Cited by 2 | Viewed by 971
Abstract
To address the challenges in identifying effective fault features and achieving sufficient diagnostic accuracy and robustness in variable-speed printing press bearings, where complex mixed-condition vibration signals exhibit non-stationarity, strong nonlinearity, ambiguous time-frequency characteristics, and overlapping fault features across multiple operating conditions, this paper [...] Read more.
To address the challenges in identifying effective fault features and achieving sufficient diagnostic accuracy and robustness in variable-speed printing press bearings, where complex mixed-condition vibration signals exhibit non-stationarity, strong nonlinearity, ambiguous time-frequency characteristics, and overlapping fault features across multiple operating conditions, this paper proposes an adaptive optimization signal decomposition method combined with dual-modal time-series and image deep feature fusion for variable-speed multi-condition bearing fault diagnosis. First, to overcome the strong parameter dependency and significant noise interference of traditional adaptive decomposition algorithms, the Crested Porcupine Optimization Algorithm is introduced to adaptively search for the optimal noise amplitude and integration count of ICEEMDAN for effective signal decomposition. IMF components are then screened and reorganized based on correlation coefficients and variance contribution rates to enhance fault-sensitive information. Second, multidimensional time-domain features are extracted in parallel to construct time-frequency images, forming time-sequence-image bimodal inputs that enhance fault representation across different dimensions. Finally, a dual-branch deep learning model is developed: the time-sequence branch employs gated recurrent units to capture feature evolution trends, while the image branch utilizes SE-ResNet18 with embedded channel attention mechanisms to extract deep spatial features. Multimodal feature fusion enables classification recognition. Validation using a bearing self-diagnosis dataset from variable-speed hybrid operation and the publicly available Ottawa variable-speed bearing dataset demonstrates that this method achieves high-accuracy fault identification and strong generalization capabilities across diverse variable-speed hybrid operating conditions. Full article
(This article belongs to the Special Issue Machine Learning Algorithms for Signal Processing)
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