Special Issue "Sound Processing in Real-Life Environments"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 1 May 2022.

Special Issue Editors

Prof. Dr. Manuel Rosa Zurera
E-Mail Website
Guest Editor
Signal Theory and Communications Department, University of Alcalá, 28801 Alcalá de Henares, Madrid, Spain
Interests: statistical signal processing; acoustic; speech; radar
Prof. Dr. Roberto Gil Pita
E-Mail Website
Guest Editor
Signal Theory and Communications Department, University of Alcalá, 28801 Alcalá de Henares, Madrid, Spain
Interests: pattern recognition; signal processing; audio and speech processing; emotion recognition
Prof. Dr. Manuel Utrilla Manso
E-Mail Website
Guest Editor
Signal Theory and Communications Department, University of Alcalá, 28801 Alcalá de Henares, Madrid, Spain
Interests: audio signal processing; array techniques for speech enhancement
Prof. Dr. Maria de Diego Antón
E-Mail Website
Guest Editor
Communications Department, Technical University of Valencia, 46022 Valencia, Spain
Interests: active noise; equalization; distributed sound processing; non-linear adaptive filtering

Special Issue Information

Dear Colleagues,

Sound processing has aroused interest in recent times with the appearance of applications in real life—both domestic and industrial. In this sense, applications have been developed for acoustic monitoring in smart cities and for indoor surveillance. Sound processing has also been applied to industry, providing key information to detect problems in certain systems. Other applications of interest, such as those related to the development of digital hearing aids, musical applications, automatic wildlife monitoring, or biomedical applications, are also important in this area.In all cases, tasks like sound event detection, sound sources location, and acoustic environments classification, among others, must be carried out. When working with real signals, the algorithms must deal with problems such as background noise, reverberation, interference, obstacles, and attenuation with distance, which affect the final operation of the developed application. Additionally, some applications have to be tackled with time-variant environments.This Special Issue focuses on developing sound signal processing algorithms for real-life applications, such as those mentioned above.

Prof. Dr. Manuel Rosa Zurera
Prof. Dr. Roberto Gil Pita
Prof. Dr. Manuel Utrilla Manso
Prof. Dr. Maria de Diego Antón
Guest Editors

Manuscript Submission Information

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Keywords

  • Machine learning for real-life sound applications
  • Sound enhancement for real-life applications
  • Acoustic environment classification
  • Time-variant acoustic environments
  • Microphone array development for sound detection, localization, and tracking
  • Emerging sound applications in industrial environments
  • Sound processing in smart cities and smart buildings
  • Biomedical sound applications
  • Wildlife acoustic monitoring
  • Sound processing for acoustic surveillance and security

Published Papers (1 paper)

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Research

Article
Acoustic Anomaly Detection of Mechanical Failures in Noisy Real-Life Factory Environments
Electronics 2021, 10(19), 2329; https://doi.org/10.3390/electronics10192329 - 23 Sep 2021
Viewed by 618
Abstract
Anomaly detection without employing dedicated sensors for each industrial machine is recognized as one of the essential techniques for preventive maintenance and is especially important for factories with low automatization levels, a number of which remain much larger than autonomous manufacturing lines. We [...] Read more.
Anomaly detection without employing dedicated sensors for each industrial machine is recognized as one of the essential techniques for preventive maintenance and is especially important for factories with low automatization levels, a number of which remain much larger than autonomous manufacturing lines. We have based our research on the hypothesis that real-life sound data from working industrial machines can be used for machine diagnostics. However, the sound data can be contaminated and drowned out by typical factory environmental sound, making the application of sound data-based anomaly detection an overly complicated process and, thus, the main problem we are solving with our approach. In this paper, we present a noise-tolerant deep learning-based methodology for real-life sound-data-based anomaly detection within real-world industrial machinery sound data. The main element of the proposed methodology is a generative adversarial network (GAN) used for the reconstruction of sound signal reconstruction and the detection of anomalies. The experimental results obtained in the Malfunctioning Industrial Machine Investigation and Inspection (MIMII) show the superiority of the proposed methodology over baseline approaches based on the One-Class Support Vector Machine (OC-SVM) and the Autoencoder–Decoder neural network. The proposed schematics using the unscented Kalman Filter (UKF) and the mean square error (MSE) loss function with the L2 regularization term showed an improvement of the Area Under Curve (AUC) for the noisy pump data of the pump. Full article
(This article belongs to the Special Issue Sound Processing in Real-Life Environments)
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