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Electronics
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Sound Processing in Real-Life Environments
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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: closed (1 May 2022) | Viewed by 8514

Special Issue Editors

Prof. Dr. Manuel Rosa Zurera

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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

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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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 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 (2 papers)

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18 pages, 920 KiB  
Article
Introducing the ReaLISED Dataset for Sound Event Classification
by Inma Mohino-Herranz, Joaquín García-Gómez, Miguel Aguilar-Ortega, Manuel Utrilla-Manso, Roberto Gil-Pita and Manuel Rosa-Zurera
Electronics 2022, 11(12), 1811; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11121811 - 07 Jun 2022
Cited by 2 | Viewed by 1811
Abstract
This paper presents the Real-Life Indoor Sound Event Dataset (ReaLISED), a new database which has been developed to contribute to the scientific advance by providing a large amount of real labeled indoor audio event recordings. They offer the scientific community the possibility of [...] Read more.
This paper presents the Real-Life Indoor Sound Event Dataset (ReaLISED), a new database which has been developed to contribute to the scientific advance by providing a large amount of real labeled indoor audio event recordings. They offer the scientific community the possibility of testing Sound Event Classification (SEC) algorithms. The full set is made up of 2479 sound clips of 18 different events, which were recorded following a precise recording process described along the proposal. This, together with a described way of testing the similarity of new audio, makes the dataset scalable and opens up the door to its future growth, if desired by the researchers. The full set presents a good balance in terms of the number of recordings of each type of event, which is a desirable characteristic of any dataset. Conversely, the main limitation of the provided data is that all the audio is recorded in indoor environments, which was the aim behind this development. To test the quality of the dataset, both the intraclass and the interclass similarities were evaluated. The first has been studied through the calculation of the intraclass Pearson correlation coefficient and further discard of redundant audio, while the second one has been evaluated with the creation, training and testing of different classifiers: linear and quadratic discriminants, k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Multilayer Perceptron (MLP), and Deep Neural Networks (DNN). Firstly, experiments were carried out over the entire dataset, and later over three different groups (impulsive sounds, non-impulsive sounds, and appliances) composed of six classes according to the results from the entire dataset. This clustering shows the usefulness of following a two-step classification process. Full article
(This article belongs to the Special Issue Sound Processing in Real-Life Environments)
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23 pages, 8613 KiB  
Article
Acoustic Anomaly Detection of Mechanical Failures in Noisy Real-Life Factory Environments
by Yuki Tagawa, Rytis Maskeliūnas and Robertas Damaševičius
Electronics 2021, 10(19), 2329; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10192329 - 23 Sep 2021
Cited by 28 | Viewed by 5919
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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