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Detection and Feature Extraction in Acoustic Sensor Signals-2nd Edition
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Detection and Feature Extraction in Acoustic Sensor Signals-2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 1988

Special Issue Editors

Dr. Yuxing Li

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Guest Editor
School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Interests: fractal dimension; underwater signal processing; sensor signal processing; denoising; feature extraction; fault diagnosis; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Fredianelli

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Guest Editor
Institute of Chemical and Physical Processes of National Research Council, Via G. Moruzzi 1, 56124 Pisa, Italy
Interests: environmental acoustics; noise mitigations; noise management; noise measurements; noise mapping; noise action plans; wind turbine noise; road traffic noise; railway noise; airport noise
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Acoustic sensors have an extremely wide range of applications in many fields, including underwater acoustics, architectural acoustics, engineering acoustics, physical acoustics, environmental acoustics, psychoacoustics, and so on. The signals collected by high-sensitivity acoustic sensors contain a large amount of valid information that facilitates further processing of the collected acoustic signals. In particular, detection and feature extraction, as two important measures of acoustic sensor signal processing, can capture more information about the target and extract features with separability.

Various trends indicate that detection as well as feature extraction play an increasingly important role in the processing of acoustic sensor signals, and presentations of the latest methods for acoustic signal detection or feature extraction are welcome for submission to this Special Issue, such as the application of stochastic resonance in vibration signals, nonlinear feature extraction of underwater acoustic signals, and so on. We encourage all authors working on similar topics to submit their work to this Special Issue. Equally welcome are contributions from any field of acoustic sensors with applications in real-world data.

Dr. Yuxing Li
Dr. Luca Fredianelli
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. Sensors 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 2600 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

  • acoustic sensor signal feature extraction
  • acoustic sensor signal detection
  • acoustic sensor signal processing
  • stochastic resonance in vibration signals
  • nonlinear feature extraction of underwater acoustic signals
  • heart rate state detection
  • acoustic beamforming and the emerging field of acoustic cameras

Published Papers (3 papers)

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16 pages, 6770 KiB  
Article
Time Delay Study of Ultrasonic Gas Flowmeters Based on VMD–Hilbert Spectrum and Cross-Correlation
by Lingcai Kong, Liang Zhang, Hulin Guo, Ning Zhao and Xinhu Xu
Sensors 2024, 24(5), 1462; https://0-doi-org.brum.beds.ac.uk/10.3390/s24051462 - 23 Feb 2024
Viewed by 447
Abstract
The accuracy of ultrasonic flowmeter time delay measurement is directly affected by the processing method of the ultrasonic echo signal. This paper proposes a method for estimating the time delay of the ultrasonic gas flowmeter based on the Variational Mode Decomposition (VMD)–Hilbert Spectrum [...] Read more.
The accuracy of ultrasonic flowmeter time delay measurement is directly affected by the processing method of the ultrasonic echo signal. This paper proposes a method for estimating the time delay of the ultrasonic gas flowmeter based on the Variational Mode Decomposition (VMD)–Hilbert Spectrum and Cross-Correlation (CC). The method improves the accuracy of the ultrasonic gas flowmeter by enhancing the quality of the echo signal. To denoise forward and reverse ultrasonic echo signals collected at various wind speeds, a Butterworth filter is initially used. The ultrasonic echo signals are then analyzed by Empirical Mode De-composition (EMD) and VMD analysis to obtain the Intrinsic Mode Function (IMF) containing distinct center frequencies, respectively. The Hilbert spectrum time–frequency diagram is used to evaluate the results of the VMD and EMD decompositions. It is found that the IMF decomposed by VMD has a better filtering performance and better anti-interference performance. Therefore, the IMF with a better effect is selected for signal reconstruction. The ultrasonic time delay is then calculated using the Cross-Correlation algorithm. The self-developed ultrasonic gas flowmeter was tested on the experimental platform of the gas flow standard devices using this signal processing method. The results show a maximum indication error of 0.84% within the flow range of 60–606 m3/h, with a repeatability of no more than 0.29%. These results meet the 1-level accuracy requirements as outlined in the national ultrasonic flowmeters calibration regulation JJG1030-2007. Full article
(This article belongs to the Special Issue Detection and Feature Extraction in Acoustic Sensor Signals-2nd Edition)
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19 pages, 16321 KiB  
Article
A Novel Joint Denoising Method for Hydrophone Signal Based on Improved SGMD and WT
by Tianyu Xing, Xiaohao Wang, Kai Ni and Qian Zhou
Sensors 2024, 24(4), 1340; https://0-doi-org.brum.beds.ac.uk/10.3390/s24041340 - 19 Feb 2024
Viewed by 492
Abstract
Underwater acoustic technology as an important means of exploring the oceans is receiving more attention. Denoising for underwater acoustic information in complex marine environments has become a hot research topic. In order to realize the hydrophone signal denoising, this paper proposes a joint [...] Read more.
Underwater acoustic technology as an important means of exploring the oceans is receiving more attention. Denoising for underwater acoustic information in complex marine environments has become a hot research topic. In order to realize the hydrophone signal denoising, this paper proposes a joint denoising method based on improved symplectic geometry modal decomposition (ISGMD) and wavelet threshold (WT). Firstly, the energy contribution (EC) is introduced into the SGMD as an iterative termination condition, which efficiently improves the denoising capability of SGMD and generates a reasonable number of symplectic geometry components (SGCs). Then spectral clustering (SC) is used to accurately aggregate SGCs into information clusters mixed-clusters, and noise clusters. Spectrum entropy (SE) is used to distinguish clusters quickly. Finally, the mixed clusters achieve the signal denoising by wavelet threshold. The useful information is reconstructed to achieve the original signal denoising. In the simulation experiment, the denoising effect of different denoising algorithms in the time domain and frequency domain is compared, and SNR and RMSE are used as evaluation indexes. The results show that the proposed algorithm has better performance. In the experiment of hydrophone, the denoising ability of the proposed algorithm is also verified. Full article
(This article belongs to the Special Issue Detection and Feature Extraction in Acoustic Sensor Signals-2nd Edition)
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25 pages, 9845 KiB  
Article
A Multi-Sensor Stochastic Energy-Based Vibro-Localization Technique with Byzantine Sensor Elimination
by Murat Ambarkutuk, Sa’ed Alajlouni, Pablo A. Tarazaga and Paul E. Plassmann
Sensors 2023, 23(23), 9309; https://0-doi-org.brum.beds.ac.uk/10.3390/s23239309 - 21 Nov 2023
Cited by 1 | Viewed by 638
Abstract
This paper presents an occupant localization technique that determines the location of individuals in indoor environments by analyzing the structural vibrations of the floor caused by their footsteps. Structural vibration waves are difficult to measure as they are influenced by various factors, including [...] Read more.
This paper presents an occupant localization technique that determines the location of individuals in indoor environments by analyzing the structural vibrations of the floor caused by their footsteps. Structural vibration waves are difficult to measure as they are influenced by various factors, including the complex nature of wave propagation in heterogeneous and dispersive media (such as the floor) as well as the inherent noise characteristics of sensors observing the vibration wavefronts. The proposed vibration-based occupant localization technique minimizes the errors that occur during the signal acquisition time. In this process, the likelihood function of each sensor—representing where the occupant likely resides in the environment—is fused to obtain a consensual localization result in a collective manner. In this work, it becomes evident that the above sources of uncertainties can render certain sensors deceptive, commonly referred to as “Byzantines.” Because the ratio of Byzantines among the set sensors defines the success of the collective localization results, this paper introduces a Byzantine sensor elimination (BSE) algorithm to prevent the unreliable information of Byzantine sensors from affecting the location estimations. This algorithm identifies and eliminates sensors that generate erroneous estimates, preventing the influence of these sensors on the overall consensus. To validate and benchmark the proposed technique, a set of previously conducted controlled experiments was employed. The empirical results demonstrate the proposed technique’s significant improvement (3~0%) over the baseline approach in terms of both accuracy and precision. Full article
(This article belongs to the Special Issue Detection and Feature Extraction in Acoustic Sensor Signals-2nd Edition)
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