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Entropy and Information Theory in Acoustics III

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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: closed (16 December 2022) | Viewed by 20044

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Dr. Yuxing Li

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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
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Special Issue Information

Dear Colleagues,

Acoustics is one of the most popular fields of research in the 21st century and has received worldwide attention, mainly in underwater acoustics, architectural acoustics, engineering acoustics, physical acoustics, environmental acoustics, psychoacoustics, musical acoustics, etc. Likewise, entropy and information theory have also been popular in recent years and can be used to quantify the complexity of a system or a period time series, which play a variety of roles in the field of acoustics, such as the feature extraction, noise reduction, condition monitoring and target tracking of acoustic signals. Any manuscripts on the application of entropy and information theory in the field of acoustics are welcome. We encourage all authors engaged in relevant research to submit their works to this Special Issue, the scope of which includes but is not limited to entropy in acoustics, information theory in acoustics and entropy and information theory in acoustics. Potential topics include, but are not limited to: underwater acoustics, architectural acoustics, engineering acoustics, physical acoustics, environmental acoustics, psychological acoustics, musical acoustics, acoustic materials, acoustic sensing, acoustic imaging, acoustic signal processing, artificial intelligence in acoustics, and deep learning in acoustics.

Dr. Yuxing Li
Guest Editor

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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. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

entropy
information theory
acoustics
underwater acoustics
architectural acoustics
engineering acoustics
physical acoustics
environmental acoustics
psychological acoustics
musical acoustics
acoustic materials
acoustic sensing
acoustic imaging
acoustic signal processing
artificial intelligence in acoustics
deep learning in acoustics

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Related Special Issues

Entropy and Information Theory in Acoustics in Entropy (10 articles)
Entropy and Information Theory in Acoustics II in Entropy (9 articles)

Published Papers (9 papers)

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Research

14 pages, 3915 KiB

Open AccessArticle

Sound Identification Method for Gas and Coal Dust Explosions Based on MLP

by Xingchen Yu and Xiaowei Li

Entropy 2023, 25(8), 1184; https://doi.org/10.3390/e25081184 - 9 Aug 2023

Viewed by 1153

Abstract

To solve the problems of backward gas and coal dust explosion alarm technology and single monitoring means in coal mines, and to improve the accuracy of gas and coal dust explosion identification in coal mines, a sound identification method for gas and coal dust explosions based on MLP in coal mines is proposed, and the distributions of the mean value of the short-time energy, zero crossing rate, spectral centroid, spectral spread, roll-off, 16-dimensional time-frequency features, MFCC, GFCC, short-time Fourier coefficients of gas explosion sound, coal dust sound, and other underground sounds were analyzed. In order to select the most suitable feature vector to characterize the sound signal, the best feature extraction model of the Relief algorithm was established, and the cross-entropy distribution of the MLP model trained with the different numbers of feature values was analyzed. In order to further optimize the feature value selection, the recognition results of the recognition models trained with the different numbers of sound feature values were compared, and the first 35-dimensional feature values were finally determined as the feature vector to characterize the sound signal. The feature vectors are input into the MLP to establish the sound recognition model of coal mine gas and coal dust explosion. An analysis of the feature extraction, optimal feature extraction, model training, and time consumption for model recognition during the model establishment process shows that the proposed algorithm has high computational efficiency and meets the requirement of the real-time coal mine safety monitoring and alarm system. From the results of recognition experiments, the sound recognition algorithm can distinguish each kind of sound involved in the experiments more accurately. The average recognition rate, recall rate, and accuracy rate of the model can reach 95%, 95%, and 95.8%, respectively, which is obviously better than the comparison algorithm and can meet the requirements of coal mine gas and coal dust explosion sensing and alarming. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics III)

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20 pages, 1325 KiB

Open AccessArticle

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication

by Xiaohui Yao, Honghui Yang and Meiping Sheng

Entropy 2023, 25(7), 1096; https://doi.org/10.3390/e25071096 - 21 Jul 2023

Cited by 3 | Viewed by 1698

Abstract

Automatic modulation classification (AMC) of underwater acoustic communication signals is of great significance in national defense and marine military. Accurate modulation classification methods can make great contributions to accurately grasping the parameters and characteristics of enemy communication systems. While a poor underwater acoustic channel makes it difficult to classify the modulation types correctly. Feature extraction and deep learning methods have proven to be effective methods for the modulation classification of underwater acoustic communication signals, but their performance is still limited by the complex underwater communication environment. Graph convolution networks (GCN) can learn the graph structured information of the data, making it an effective method for processing structured data. To improve the stability and robustness of AMC in underwater channels, we combined the feature extraction and deep learning methods by fusing the multi-domain features and deep features using GCN. The proposed method takes the relationships among the different multi-domain features and deep features into account. Firstly, a feature graph was built using the properties of the features. Secondly, multi-domain features were extracted from the received signals and deep features were extracted from the signals using a deep neural network. Thirdly, we constructed the input of GCN using these features and the graph. Then, the multi-domain features and deep features were fused by the GCN. Finally, we classified the modulation types using the output of GCN by way of a softmax layer. We conducted the experiments on a simulated dataset and a real-world dataset, respectively. The results show that the AMC based on GCN can achieve a significant improvement in performance compared to the current state-of-the-art methods. Our approach is robust in underwater acoustic channels. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics III)

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17 pages, 5758 KiB

Open AccessArticle

Comparative Study on Feature Extraction of Marine Background Noise Based on Nonlinear Dynamic Features

by Guanni Ji, Yu Wang and Fei Wang

Entropy 2023, 25(6), 845; https://doi.org/10.3390/e25060845 - 25 May 2023

Cited by 4 | Viewed by 1491

Abstract

Marine background noise (MBN) is the background noise of the marine environment, which can be used to invert the parameters of the marine environment. However, due to the complexity of the marine environment, it is difficult to extract the features of the MBN. In this paper, we study the feature extraction method of MBN based on nonlinear dynamics features, where the nonlinear dynamical features include two main categories: entropy and Lempel–Ziv complexity (LZC). We have performed single feature and multiple feature comparative experiments on feature extraction based on entropy and LZC, respectively: for entropy-based feature extraction experiments, we compared feature extraction methods based on dispersion entropy (DE), permutation entropy (PE), fuzzy entropy (FE), and sample entropy (SE); for LZC-based feature extraction experiments, we compared feature extraction methods based on LZC, dispersion LZC (DLZC) and permutation LZC (PLZC), and dispersion entropy-based LZC (DELZC). The simulation experiments prove that all kinds of nonlinear dynamics features can effectively detect the change of time series complexity, and the actual experimental results show that regardless of the entropy-based feature extraction method or LZC-based feature extraction method, they both present better feature extraction performance for MBN. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics III)

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19 pages, 11296 KiB

Open AccessArticle

A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition

by Zipeng Li, Kunde Yang, Xingyue Zhou and Shunli Duan

Entropy 2023, 25(4), 669; https://doi.org/10.3390/e25040669 - 16 Apr 2023

Cited by 5 | Viewed by 1594

Abstract

As is well-known, ship-radiated noise (SN) signals, which contain a large number of ship operating characteristics and condition information, are widely used in ship recognition and classification. However, it is still a great challenge to extract weak operating characteristics from SN signals because of heavy noise and non-stationarity. Therefore, a new mono-component extraction method is proposed in this paper for taxonomic purposes. First, the non-local means algorithm (NLmeans) is proposed to denoise SN signals without destroying its time-frequency structure. Second, adaptive chirp mode decomposition (ACMD) is modified and applied on denoised signals to adaptively extract mono-component modes. Finally, sub-signals are selected based on spectral kurtosis (SK) and then analyzed for ship recognition and classification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics III)

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12 pages, 3497 KiB

Open AccessArticle

Sound Recognition Method of Coal Mine Gas and Coal Dust Explosion Based on GoogLeNet

by Xingchen Yu and Xiaowei Li

Entropy 2023, 25(3), 412; https://doi.org/10.3390/e25030412 - 24 Feb 2023

Cited by 5 | Viewed by 1683

Abstract

To solve the problems of backward means of coal mine gas and coal dust explosion monitoring, late reporting, and low leakage rate, a sound recognition method of coal mine gas and coal dust explosion based on GoogLeNet was proposed. After installing mining pickups in key monitoring areas of coal mines to collect the sounds of the working equipment and the environment, the collected sound was analyzed by continuous wavelet to obtain its scale coefficient map. This was then imported into GoogLeNet to obtain the recognition model of coal mine gas and coal dust explosions. The test sound was obtained by continuous wavelet analysis to obtain the scale coefficient map, brought into the completed training recognition model to obtain the sound signal class, and verified by experiment. Firstly, the scale coefficient map extracted from the sound signal by continuous wavelet analysis showed that the similarity between the subjective and objective indicators of the wavelet coefficient maps of the gas explosion sound and coal dust explosion sound was higher, but the difference between these and the rest of the coal mine sounds was clearer, helping to effectively distinguish gas and coal dust explosion sounds from other sounds. Secondly, the experimental results of GoogLeNet parameters can be obtained. When the dropout parameter is 0.5 and the initial learning rate is 0.001, the recognition effect of the model established by GoogLeNet was optimal. According to the selected parameters, the training loss, testing loss, training recognition rate, and testing recognition rate of the model are all in line with expectations. Finally, the experimental recognition results show that the recognition rate of the proposed method is 97.38%, the recall rate is 86.1%, and the accuracy rate is 100% for the case of a 9:1 ratio of test data to training data, and the overall recognition effect of the proposed GoogLeNet is significantly better than that of vgg and Alexnet, which can effectively solve the problem of under-sampling of coal mine gas and coal dust explosion sounds and can meet the need for the intelligent recognition of coal mine gas and dust explosions. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics III)

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22 pages, 6165 KiB

Open AccessArticle

Automatic Modulation Classification for Underwater Acoustic Communication Signals Based on Deep Complex Networks

by Xiaohui Yao, Honghui Yang and Meiping Sheng

Entropy 2023, 25(2), 318; https://doi.org/10.3390/e25020318 - 9 Feb 2023

Cited by 10 | Viewed by 2433

Abstract

Automatic modulation classification (AMC) is an important method for monitoring and identifying any underwater communication interference. Since the underwater acoustic communication scenario is full of multi-path fading and ocean ambient noise (OAN), coupled with the application of modern communication technology, which is usually susceptible to environmental influences, automatic modulation classification (AMC) becomes particularly difficult when it comes to an underwater scenario. Motivated by the deep complex networks (DCN), which have an innate ability to process complex data, we explore DCN for AMC of underwater acoustic communication signals. To integrate the signal processing method with deep learning and overcome the influences of underwater acoustic channels, we propose two complex physical signal processing layers based on DCN. The proposed layers include a deep complex matched filter (DCMF) and deep complex channel equalizer (DCCE), which are designed to remove noise and reduce the influence of multi-path fading for the received signals, respectively. Hierarchical DCN is constructed using the proposed method to achieve better performance of AMC. The influence of the real-world underwater acoustic communication scenario is taken into account; two underwater acoustic multi-path fading channels are conducted using the real-world ocean observation dataset, white Gaussian noise, and real-world OAN are used as the additive noise, respectively. Contrastive experiments show that the AMC based on DCN can achieve better performance than the traditional deep neural network based on real value (the average accuracy of the DCN is 5.3% higher than real-valued DNN). The proposed method based on DCN can effectively reduce the influence of underwater acoustic channels and improve the AMC performance in different underwater acoustic channels. The performance of the proposed method was verified on the real-world dataset. In the underwater acoustic channels, the proposed method outperforms a series of advanced AMC method. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics III)

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16 pages, 3354 KiB

Open AccessArticle

Underwater Acoustic Target Recognition Based on Attention Residual Network

by Juan Li, Baoxiang Wang, Xuerong Cui, Shibao Li and Jianhang Liu

Entropy 2022, 24(11), 1657; https://doi.org/10.3390/e24111657 - 15 Nov 2022

Cited by 17 | Viewed by 3071

Abstract

Underwater acoustic target recognition is very complex due to the lack of labeled data sets, the complexity of the marine environment, and the interference of background noise. In order to enhance it, we propose an attention-based residual network recognition method (AResnet). The method can be used to identify ship-radiated noise in different environments. Firstly, a residual network is used to extract the deep abstract features of three-dimensional fusion features, and then a channel attention module is used to enhance different channels. Finally, the features are classified by the joint supervision of cross-entropy and central loss functions. At the same time, for the recognition of ship-radiated noise in other environments, we use the pre-training network AResnet to extract the deep acoustic features and apply the network structure to underwater acoustic target recognition after fine-tuning. The two sets of ship radiation noise datasets are verified, the DeepShip dataset is trained and verified, and the average recognition accuracy is 99%. Then, the trained AResnet structure is fine-tuned and applied to the ShipsEar dataset. The average recognition accuracy is 98%, which is better than the comparison method. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics III)

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17 pages, 3915 KiB

Open AccessArticle

Optimized Ship-Radiated Noise Feature Extraction Approaches Based on CEEMDAN and Slope Entropy

by Yuxing Li, Bingzhao Tang and Shangbin Jiao

Entropy 2022, 24(9), 1265; https://doi.org/10.3390/e24091265 - 8 Sep 2022

Cited by 30 | Viewed by 2429

Abstract

Slope entropy (Slopen) has been demonstrated to be an excellent approach to extracting ship-radiated noise signals (S-NSs) features by analyzing the complexity of the signals; however, its recognition ability is limited because it extracts the features of undecomposed S-NSs. To solve this problem, in this study, we combined complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to explore the differences of Slopen between the intrinsic mode components (IMFs) of the S-NSs and proposed a single-IMF optimized feature extraction approach. Aiming to further enhance its performance, the optimized combination of dual-IMFs was selected, and a dual-IMF optimized feature extraction approach was also proposed. We conducted three experiments to demonstrate the effectiveness of CEEMDAN, Slopen, and the proposed approaches. The experimental and comparative results revealed both of the proposed single- and dual-IMF optimized feature extraction approaches based on Slopen and CEEMDAN to be more effective than the original ship signal-based and IMF-based feature extraction approaches. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics III)

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18 pages, 6155 KiB

Open AccessArticle

Wind-Direction Estimation from Single X-Band Marine Radar Image Improvement by Utilizing the DWT and Azimuth-Scale Expansion Method

by Huanyu Yu, Hui Wang and Zhizhong Lu

Entropy 2022, 24(6), 747; https://doi.org/10.3390/e24060747 - 24 May 2022

Cited by 2 | Viewed by 1890

Abstract

In this study, a method based on the discrete wavelet transform (DWT) and azimuth-scale expansion is presented to retrieve the sea-surface wind direction from a single X-band marine radar image. The algorithm first distinguishes rain-free and rain-contaminated radar images based on the occlusion zero-pixel percentage and then discards the rain-contaminated images. The radar image whose occlusion areas have been removed is decomposed into different low-frequency sub-images by the 2D DWT, and the appropriate low-frequency sub-image is selected. Images collected with a standard marine HH-polarized X-band radar operating at grazing incidence display a single intensity peak in the upwind direction. To overcome the influence of the occlusion area, before determining the wind direction, the data near the ship bow are shifted to expand the azimuth scale of the data. Finally, a harmonic function is least-square-fitted to the range-averaged radar return of the low-frequency sub-image as a function of the antenna look azimuth to determine the wind direction. Different from the wind-direction retrieval algorithms previously presented, this method is more suitable for sailing ships, as it functions well even if the radar data are heavily blocked. The results show that compared with the single-curve fitting algorithm, the algorithm based on DWT and azimuth-scale expansion can improve the wind-direction results in sailing ships, showing a reduction of 7.84° in the root-mean-square error with respect to the reference. Full article

(This article belongs to the Special Issue Entropy and Information Theory in Acoustics III)

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