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Detection and Feature Extraction in Acoustic Sensor Signals

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

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 24328

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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 the latest methods for acoustic signal detection or feature extraction are welcome in 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

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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

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Published Papers (12 papers)

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Editorial

Jump to: Research

2 pages, 163 KiB

Open AccessEditorial

Detection and Feature Extraction in Acoustic Sensor Signals

by Yuxing Li and Luca Fredianelli

Sensors 2023, 23(19), 8030; https://doi.org/10.3390/s23198030 - 22 Sep 2023

Viewed by 1298

Abstract

Our advances in detection and feature extraction in the processing of acoustic signals allow us to capture more information about a target and extract features with separability [...] Full article

(This article belongs to the Special Issue Detection and Feature Extraction in Acoustic Sensor Signals)

Research

Jump to: Editorial

15 pages, 736 KiB

Open AccessArticle

An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation

by Xinyu Zhang, Mengjiao Ren, Jiemin Duan, Yingmin Yi, Biyu Lei and Shuyue Wu

Sensors 2023, 23(14), 6603; https://doi.org/10.3390/s23146603 - 22 Jul 2023

Cited by 2 | Viewed by 1080

Abstract

Although the cost-reference particle filter (CRPF) has a good advantage in solving the state estimation problem with unknown noise statistical characteristics, its estimation accuracy is still affected by the lack of particle diversity and sensitivity to the particles’ initial value. In order to solve these problems of the CRPF, this paper proposed an intelligent cost-reference particle filter algorithm based on multi-population cooperation. A multi-population cooperative resampling strategy based on ring structure was designed. The particles were divided into multiple independent populations upon initialization, and each population generated particles with a different initial distribution. The particles in each population were divided into three different particle sets with high, medium and low weights by the golden section ratio according to the weight. The particle sets with high and medium weights were retained. Then, a cooperative strategy based on Gaussian mutation was designed to resample the low-weight particle set of each population. The high-weight particles of the previous population in the ring structure were randomly selected for Gaussian mutation to replace the low-weight particles in the current population. The low-weight particles of all populations were resampled in turn. The simulation results show that the intelligent CRPF based on multi-population cooperation proposed in this paper can reduce the sensitivity of the CRPF to the particles’ initial value and improve the particle diversity in resampling. Compared with the general CRPF and intelligent CRPF with adaptive MH resampling (MH-CRPF), the RMSE and MAE of the proposed method are lower. Full article

(This article belongs to the Special Issue Detection and Feature Extraction in Acoustic Sensor Signals)

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24 pages, 7948 KiB

Open AccessArticle

Bearing Fault-Detection Method Based on Improved Grey Wolf Algorithm to Optimize Parameters of Multistable Stochastic Resonance

by Weichao Huang and Ganggang Zhang

Sensors 2023, 23(14), 6529; https://doi.org/10.3390/s23146529 - 19 Jul 2023

Cited by 6 | Viewed by 1502

Abstract

In an effort to overcome the problem that the traditional stochastic resonance system cannot adjust the structural parameters adaptively in bearing fault-signal detection, this article proposes an adaptive-parameter bearing fault-detection method. First of all, the four strategies of Sobol sequence initialization, exponential convergence factor, adaptive position update, and Cauchy–Gaussian hybrid variation are used to improve the basic grey wolf optimization algorithm, which effectively improves the optimization performance of the algorithm. Then, based on the multistable stochastic resonance model, the structure parameters of the multistable stochastic resonance are optimized through improving the grey wolf algorithm, so as to enhance the fault signal and realize the effective detection of the bearing fault signal. Finally, the proposed bearing fault-detection method is used to analyze and diagnose two open-source bearing data sets, and comparative experiments are conducted with the optimization results of other improved algorithms. Meanwhile, the method proposed in this paper is used to diagnose the fault of the bearing in the lifting device of a single-crystal furnace. The experimental results show that the fault frequency of the inner ring of the first bearing data set diagnosed using the proposed method was 158 Hz, and the fault frequency of the outer ring of the second bearing data set diagnosed using the proposed method was 162 Hz. The fault-diagnosis results of the two bearings were equal to the results derived from the theory. Compared with the optimization results of other improved algorithms, the proposed method has a faster convergence speed and a higher output signal-to-noise ratio. At the same time, the fault frequency of the bearing of the lifting device of the single-crystal furnace was effectively diagnosed as 35 Hz, and the bearing fault signal was effectively detected. Full article

(This article belongs to the Special Issue Detection and Feature Extraction in Acoustic Sensor Signals)

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19 pages, 4526 KiB

Open AccessArticle

Intelligent Diagnosis of Rolling Bearings Fault Based on Multisignal Fusion and MTF-ResNet

by Kecheng He, Yanwei Xu, Yun Wang, Junhua Wang and Tancheng Xie

Sensors 2023, 23(14), 6281; https://doi.org/10.3390/s23146281 - 10 Jul 2023

Cited by 6 | Viewed by 1575

Abstract

Existing diagnosis methods for bearing faults often neglect the temporal correlation of signals, resulting in easy loss of crucial information. Moreover, these methods struggle to adapt to complex working conditions for bearing fault feature extraction. To address these issues, this paper proposes an intelligent diagnosis method for compound faults in metro traction motor bearings. This method combines multisignal fusion, Markov transition field (MTF), and an optimized deep residual network (ResNet) to enhance the accuracy and effectiveness of diagnosis in the presence of complex working conditions. At the outset, the acquired vibration and acoustic emission signals are encoded into two-dimensional color feature images with temporal relevance by Markov transition field. Subsequently, the image features are extracted and fused into a set of comprehensive feature images with the aid of the image fusion framework based on a convolutional neural network (IFCNN). Afterwards, samples representing different fault types are presented as inputs to the optimized ResNet model during the training phase. Through this process, the model’s ability to achieve intelligent diagnosis of compound faults in variable working conditions is realized. The results of the experimental analysis verify that the proposed method can effectively extract comprehensive fault features while working in complex conditions, enhancing the efficiency of the detection process and achieving a high accuracy rate for the diagnosis of compound faults. Full article

(This article belongs to the Special Issue Detection and Feature Extraction in Acoustic Sensor Signals)

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18 pages, 4975 KiB

Open AccessArticle

A Dual-Optimization Fault Diagnosis Method for Rolling Bearings Based on Hierarchical Slope Entropy and SVM Synergized with Shark Optimization Algorithm

by Yuxing Li, Bingzhao Tang, Bo Huang and Xiaohui Xue

Sensors 2023, 23(12), 5630; https://doi.org/10.3390/s23125630 - 16 Jun 2023

Cited by 5 | Viewed by 1543

Abstract

Slope entropy (SlopEn) has been widely applied in fault diagnosis and has exhibited excellent performance, while SlopEn suffers from the problem of threshold selection. Aiming to further enhance the identifying capability of SlopEn in fault diagnosis, on the basis of SlopEn, the concept of hierarchy is introduced, and a new complexity feature, namely hierarchical slope entropy (HSlopEn), is proposed. Meanwhile, to address the problems of the threshold selection of HSlopEn and a support vector machine (SVM), the white shark optimizer (WSO) is applied to optimize both HSlopEn and an SVM, and WSO-HSlopEn and WSO-SVM are proposed, respectively. Then, a dual-optimization fault diagnosis method for rolling bearings based on WSO-HSlopEn and WSO-SVM is put forward. We conducted measured experiments on single- and multi-feature scenarios, and the experimental results demonstrated that whether single-feature or multi-feature, the WSO-HSlopEn and WSO-SVM fault diagnosis method has the highest recognition rate compared to other hierarchical entropies; moreover, under multi-features, the recognition rates are all higher than 97.5%, and the more features we select, the better the recognition effect. When five nodes are selected, the highest recognition rate reaches 100%. Full article

(This article belongs to the Special Issue Detection and Feature Extraction in Acoustic Sensor Signals)

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14 pages, 3314 KiB

Open AccessArticle

Deconvolved Fractional Fourier Domain Beamforming for Linear Frequency Modulation Signals

by Zhuoran Liu, Quan Tao, Wanzhong Sun and Xiaomei Fu

Sensors 2023, 23(7), 3511; https://doi.org/10.3390/s23073511 - 27 Mar 2023

Cited by 2 | Viewed by 1448

Abstract

To estimate the direction of arrival (DOA) of a linear frequency modulation (LFM) signal in a low signal-to-noise ratio (SNR) hydroacoustic environment by a small aperture array, a novel deconvolved beamforming method based on fractional Fourier domain delay-and-sum beamforming (FrFB) was proposed. Fractional Fourier transform (FrFT) was used to convert the received signal into the fractional Fourier domain, and delay-and-sum beamforming was subsequently performed. Noise resistance was acquired by focusing the energy of the LFM signal distributed in the time–frequency domain. Then, according to the convolution structure of the FrFB complex output, the influence of the fractional Fourier domain complex beam pattern was removed by deconvolution, and the target spatial distribution was restored. Therefore, an improved spatial resolution of DOA estimation was obtained without increasing the array aperture. The simulation and experimental results show that, with a small aperture array at low SNR, the proposed method possesses higher spatial resolution than FrFB and frequency-domain deconvolved conventional beamforming. Full article

(This article belongs to the Special Issue Detection and Feature Extraction in Acoustic Sensor Signals)

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20 pages, 5207 KiB

Open AccessArticle

Leaky Partial Update LMS Algorithms in Application to Structural Active Noise Control

by Dariusz Bismor

Sensors 2023, 23(3), 1169; https://doi.org/10.3390/s23031169 - 19 Jan 2023

Cited by 5 | Viewed by 1776

Abstract

Adaptive signal processing algorithms play an important role in many practical applications in diverse fields, such as telecommunication, radar, sonar, multimedia, biomedical engineering and noise control. Recently, a group of adaptive filtering algorithms called partial update adaptive algorithms (partial updates) has gathered considerable attention in both research and practical applications. This paper is a study of the application of PUs to very demanding, structural active noise control (ANC) systems, which are of particular interest due to their ability to provide for a global noise reduction. However, such systems are multichannel, with very high computational power requirements, which may be reduced by the application of partial updates. The paper discusses the modifications necessary to apply PUs in structural ANC systems and the potential computational power savings offered by this application. As a result, leaky versions of the PU LMS algorithms are introduced to the general public. The paper also presents two simulation examples, based on real laboratory setups, confirming high performance of the proposed algorithms. Full article

(This article belongs to the Special Issue Detection and Feature Extraction in Acoustic Sensor Signals)

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18 pages, 2443 KiB

Open AccessArticle

Statistical Pass-By for Unattended Road Traffic Noise Measurement in an Urban Environment

by Elena Ascari, Mauro Cerchiai, Luca Fredianelli and Gaetano Licitra

Sensors 2022, 22(22), 8767; https://doi.org/10.3390/s22228767 - 13 Nov 2022

Cited by 26 | Viewed by 2556

Abstract

Low-noise surfaces have become a common mitigation action in the last decade, so much so that different methods for feature extraction have been established to evaluate their efficacy. Among these, the Close Proximity Index (CPX) evaluates the noise emissions by means of multiple runs at different speeds performed with a vehicle equipped with a reference tire and with acoustic sensors close to the wheel. However, signals acquired with CPX make it source oriented, and the analysis does not consider the real traffic flow of the studied site for a receiver-oriented approach. These aspects are remedied by Statistical Pass-By (SPB), a method based on sensor feature extraction with live detection of events; noise and speed acquisitions are performed at the roadside in real case scenarios. Unfortunately, the specific SPB requirements for its measurement setup do not allow an evaluation in urban context unless a special setup is used, but this may alter the acoustical context in which the measurement was performed. The present paper illustrates the testing and validation of a method named Urban Pass-By (U-SPB), developed during the LIFE NEREiDE project. U-SPB originates from standard SPB, exploits unattended measurements and develops an in-lab feature detection and extraction procedure. The U-SPB extends the evaluation in terms of before/after data comparison of the efficiency of low-noise laying in an urban context while combining the estimation of long-term noise levels and traffic parameters for other environmental noise purposes, such as noise mapping and action planning. Full article

(This article belongs to the Special Issue Detection and Feature Extraction in Acoustic Sensor Signals)

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16 pages, 37874 KiB

Open AccessArticle

Optimization Algorithm for Delay Estimation Based on Singular Value Decomposition and Improved GCC-PHAT Weighting

by Shizhe Wang, Zongji Li, Pingbo Wang and Huadong Chen

Sensors 2022, 22(19), 7254; https://doi.org/10.3390/s22197254 - 24 Sep 2022

Cited by 6 | Viewed by 1979

Abstract

The accuracy of time delay estimation seriously affects the accuracy of sound source localization. In order to improve the accuracy of time delay estimation under the condition of low SNR, a delay estimation optimization algorithm based on singular value decomposition and improved GCC [...] Read more.

ρ γ

weighting) is proposed. Firstly, the acoustic signal collected by the acoustic sensor array is subjected to singular value decomposition and noise reduction processing to improve the signal-to-noise ratio of the signal; then, the cross-correlation operation is performed, and the cross-correlation function is processed by the GCC-PHAT-

ρ γ

weighting method to obtain the cross-power spectrum; finally, the inverse transformation is performed to obtain the generalized correlation time domain function, and the peak detection is performed to obtain the delay difference. The experiment was carried out in a large outdoor pool, and the experimental data were processed to compare the time delay estimation performance of three methods: GCC-PHAT weighting, SVD-GCC-PHAT weighting (meaning: GCC-PHAT weighting based on singular value decomposition) and SVD-GCC-PHAT-

ρ γ

weighting (meaning: GCC-PHAT-

ρ γ

weighting based on singular value decomposition). The results show that the delay estimation optimization algorithm based on SVD-GCC-PHAT-

ρ γ

improves the delay estimation accuracy by at least 37.95% compared with the other two methods. The new optimization algorithm has good delay estimation performance. Full article

(This article belongs to the Special Issue Detection and Feature Extraction in Acoustic Sensor Signals)

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15 pages, 3378 KiB

Open AccessArticle

A Time-of-Flight Estimation Method for Acoustic Ranging and Thermometry Based on Digital Lock-In Filtering

by Qi Liu, Bin Zhou, Jianyong Zhang, Ruixue Cheng, Xuhao Zhao, Rong Zhao, Minglu Dai, Bubin Wang and Yihong Wang

Sensors 2022, 22(15), 5519; https://doi.org/10.3390/s22155519 - 24 Jul 2022

Cited by 6 | Viewed by 1665

Abstract

Accurate ranging and real-time temperature monitoring are essential for metrology and safety in electrical conduit applications. This paper proposes an acoustic time-of-flight (TOF) estimation method based on the digital lock-in filtering (DLF) technique for conduit ranging and thermometry. The method establishes the relationship between the frequency and the time domain by applying a linear frequency modulated Chirp signal as the sound source and using the DLF technique to extract the first harmonic of the characteristic frequencies of the transmitted and received signals. Acoustic TOF estimation in the conduit is then achieved by calculating the mathematical expectation of the time difference between each characteristic frequency in the time-frequency relationship of the two signals. The experimental results with enhanced noise interference on different conduit lengths and various temperature conditions, proved that the proposed DLF method can establish a robust linear time-frequency relationship according to the characteristics of the Chirp signal, and the measurement accuracy of TOF has also been confirmed. Compared to the conventional method, the DLF method provides the lowest absolute error and standard deviation for both distance and temperature measurements with an enhanced robustness. Full article

(This article belongs to the Special Issue Detection and Feature Extraction in Acoustic Sensor Signals)

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19 pages, 3795 KiB

Open AccessArticle

A Novel Deep-Learning Method with Channel Attention Mechanism for Underwater Target Recognition

by Lingzhi Xue, Xiangyang Zeng and Anqi Jin

Sensors 2022, 22(15), 5492; https://doi.org/10.3390/s22155492 - 23 Jul 2022

Cited by 19 | Viewed by 2698

Abstract

The core of underwater acoustic recognition is to extract the spectral features of targets. The running speed and track of the targets usually result in a Doppler shift, which poses significant challenges for recognizing targets with different Doppler frequencies. This paper proposes deep learning with a channel attention mechanism approach for underwater acoustic recognition. It is based on three crucial designs. Feature structures can obtain high-dimensional underwater acoustic data. The feature extraction model is the most important. First, we develop a ResNet to extract the deep abstraction spectral features of the targets. Then, the channel attention mechanism is introduced in the camResNet to enhance the energy of stable spectral features of residual convolution. This is conducive to subtly represent the inherent characteristics of the targets. Moreover, a feature classification approach based on one-dimensional convolution is applied to recognize targets. We evaluate our approach on challenging data containing four kinds of underwater acoustic targets with different working conditions. Our experiments show that the proposed approach achieves the best recognition accuracy (98.2%) compared with the other approaches. Moreover, the proposed approach is better than the ResNet with a widely used channel attention mechanism for data with different working conditions. Full article

(This article belongs to the Special Issue Detection and Feature Extraction in Acoustic Sensor Signals)

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14 pages, 5303 KiB

Open AccessArticle

Study on a Detection Technique for Scholte Waves at the Seafloor

by Minshuai Liang, Liang Wang, Gaokun Yu, Yun Ren and Linhui Peng

Sensors 2022, 22(14), 5344; https://doi.org/10.3390/s22145344 - 18 Jul 2022

Cited by 3 | Viewed by 1906

Abstract

Scholte waves at the seafloor have significant potential for underwater detection and communication, so a study about detecting Scholte waves is very meaningful in practice. In this paper, the detection of Scholte waves at the seafloor is researched theoretically and experimentally. Acoustic models with the multilayer elastic bottom are established according to the ocean environment, and a tank experiment is designed and carried out to detect Scholte waves. Different from detecting Scholte waves in the seismic wavefield, a technique for detecting Scholte waves in the sound pressure field is proposed in this paper. The experimental results show that the proposed technique can detect Scholte waves effectively, and there are no problems such as seabed coupling and the effect of wave speeds. Furthermore, the results also show that this detection technique is still effective in conditions with a sediment layer. The existence of sediment layers changes the acoustic field conditions and affects the excitation of Scholte waves. Full article

(This article belongs to the Special Issue Detection and Feature Extraction in Acoustic Sensor Signals)

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