Recognition Of Acoustic Signals Research Articles

Modulation recognition of underwater acoustic communication signals based on neural architecture search

Modulation recognition of underwater communication signals is a critical aspect of underwater information confrontation. However, the current deep learning-based methods for underwater communication modulation recognition often mimic neural network architectures used in image processing and speech processing, tending to perform poorly in low signal-to-noise ratio (SNR) conditions. This paper introduces a novel approach by utilizing neural architecture search (NAS) method. By automatically searching for neural architectures that are well-suited for modulation recognition in underwater environment, our proposed method improves the classification performance particularly in low SNR conditions. Furthermore, we have also proposed a recognition method based on attention mechanism and feature fusion, which substantially enhances the accuracy of identifying phase-modulated signals. Numerical simulation and experimental data are used to demonstrate performance of proposed methods.

Tsallis Entropy-Based Complexity-IPE Casualty Plane: A Novel Method for Complex Time Series Analysis.

Due to its capacity to unveil the dynamic characteristics of time series data, entropy has attracted growing interest. However, traditional entropy feature extraction methods, such as permutation entropy, fall short in concurrently considering both the absolute amplitude information of signals and the temporal correlation between sample points. Consequently, this limitation leads to inadequate differentiation among different time series and susceptibility to noise interference. In order to augment the discriminative power and noise robustness of entropy features in time series analysis, this paper introduces a novel method called Tsallis entropy-based complexity-improved permutation entropy casualty plane (TC-IPE-CP). TC-IPE-CP adopts a novel symbolization approach that preserves both absolute amplitude information and inter-point correlations within sequences, thereby enhancing feature separability and noise resilience. Additionally, by incorporating Tsallis entropy and weighting the probability distribution with parameter q, it integrates with statistical complexity to establish a feature plane of complexity and entropy, further enriching signal features. Through the integration of multiscale algorithms, a multiscale Tsallis-improved permutation entropy algorithm is also developed. The simulation results indicate that TC-IPE-CP requires a small amount of data, exhibits strong noise resistance, and possesses high separability for signals. When applied to the analysis of heart rate signals, fault diagnosis, and underwater acoustic signal recognition, experimental findings demonstrate that TC-IPE-CP can accurately differentiate between electrocardiographic signals of elderly and young subjects, achieve precise bearing fault diagnosis, and identify four types of underwater targets. Particularly in underwater acoustic signal recognition experiments, TC-IPE-CP achieves a recognition rate of 96.67%, surpassing the well-known multi-scale dispersion entropy and multi-scale permutation entropy by 7.34% and 19.17%, respectively. This suggests that TC-IPE-CP is highly suitable for the analysis of complex time series.

Weak abnormal acoustic signal enhancement and recognition using squeeze-and-excitation attention based denoising convolutional neural network during high-dam flood discharging

Acoustic signals (particularly cavitation acoustic signals) generated during the flood discharge of high dams are highly sensitive to various abnormal situations, whereas weak abnormal signal recognition under strong discharge-noise interference is extremely challenging. Based on the prototype and model experiments, the related abnormal acoustic signals and discharge noise were recorded to construct datasets. Subsequently, using the framework of the deep neural network (DNN) speech enhancement method, a squeeze-and-excitation attention based denoising convolutional neural network (DnCNN) based method for weak abnormal acoustic signal enhancement and recognition was proposed and verified using two case studies of cavitation acoustic signal enhancement and multicategory acoustic signal enhancement and recognition. Compared with the DnCNN method and traditional signal processing methods (such as wavelet, empirical mode decomposition, least mean square, and recursive least square), the proposed method achieved excellent signal enhancement performance after training based on limited prior knowledge of signal and noise. It also demonstrated good generalization ability and robustness in multicategory tasks, which significantly improved the abnormal signal recognition accuracy. This study provides technical support for the practical application of acoustic monitoring based on DNN for safety during the flood discharge of high dams.

Research on Sea State Signal Recognition Based on Beluga Whale Optimization-Slope Entropy and One Dimensional-Convolutional Neural Network.

This study introduces a novel nonlinear dynamic analysis method, known as beluga whale optimization-slope entropy (BWO-SlEn), to address the challenge of recognizing sea state signals (SSSs) in complex marine environments. A method of underwater acoustic signal recognition based on BWO-SlEn and one-dimensional convolutional neural network (1D-CNN) is proposed. Firstly, particle swarm optimization-slope entropy (PSO-SlEn), BWO-SlEn, and Harris hawk optimization-slope entropy (HHO-SlEn) were used for feature extraction of noise signal and SSS. After 1D-CNN classification, BWO-SlEn were found to have the best recognition effect. Secondly, fuzzy entropy (FE), sample entropy (SE), permutation entropy (PE), and dispersion entropy (DE) were used to extract the signal features. After 1D-CNN classification, BWO-SlEn and 1D-CNN were found to have the highest recognition rate compared with them. Finally, compared with the other six recognition methods, the recognition rates of BWO-SlEn and 1D-CNN for the noise signal and SSS are at least 6% and 4.75% higher, respectively. Therefore, the BWO-SlEn and 1D-CNN recognition methods proposed in this paper are more effective in the application of SSS recognition.

An Acoustic Array Sensor Signal Recognition Algorithm for Low-Altitude Targets Using Multiple Five-Element Acoustic Positioning Systems with VMD

To solve the problem in target acoustic signal processing and recognition when the target flies at a low altitude based on the acoustic positioning system, which is often affected by external interference and brings false information, this paper proposes a target signal processing and recognition algorithm for low-altitude target acoustic positioning based on variational modal decomposition and the test method of multiple five-element acoustic arrays. This algorithm uses VMD to decompose the target signal into modal components with different central frequencies and then performs wavelet threshold processing on the low-frequency part of the signal. After determining the remaining signal components and the low-frequency part’s threshold, the residual component is reconstructed. Based on the test principle and calculation model of the five-element acoustic positioning system, following processing of the low-altitude target acoustic positioning signal using variational modal decomposition, the cross-correlation function method is introduced to perform correlation operations on the basic array of five acoustic sensors and then obtain the time value and time difference of the target acoustic information in each acoustic sensor, ultimately determining the spatial position of the target. Finally, we used the data fusion processing method for target coordinates in multi-acoustic basic arrays to determine the actual target position. By comparing the results obtained using the high-speed camera method with those of the proposed approach, it was found that the average error in the test area of 100 × 100 m was less than 1 m.

Distributed Optical Fiber Acoustic Sensing Signal Recognition Based on Improved Depth Residual Shrinkage Network

Distributed Optical Fiber Acoustic Sensing Signal Recognition Based on Improved Depth Residual Shrinkage Network

Real-Time Recognition Method for Key Signals of Rock Fracture Acoustic Emissions Based on Deep Learning.

The characteristics of acoustic emission signals generated in the process of rock deformation and fission contain rich information on internal rock damage. The use of acoustic emissions monitoring technology can analyze and identify the precursor information of rock failure. At present, in the field of acoustic emissions monitoring and the early warning of rock fracture disasters, there is no real-time identification method for a disaster precursor characteristic signal. It is easy to lose information by analyzing the characteristic parameters of traditional acoustic emissions to find signals that serve as precursors to disasters, and analysis has mostly been based on post-analysis, which leads to poor real-time recognition of disaster precursor characteristics and low application levels in the engineering field. Based on this, this paper regards the acoustic emissions signal of rock fracture as a kind of speech signal generated by rock fracture uses this idea of speech recognition for reference alongside spectral analysis (STFT) and Mel frequency analysis to realize the feature extraction of acoustic emissions from rock fracture. In deep learning, based on the VGG16 convolutional neural network and AlexNet convolutional neural network, six intelligent real-time recognition models of rock fracture and key acoustic emission signals were constructed, and the network structure and loss function of traditional VGG16 were optimized. The experimental results show that these six deep-learning models can achieve the real-time intelligent recognition of key signals, and Mel, combined with the improved VGG16, achieved the best performance with 87.68% accuracy and 81.05% recall. Then, by comparing multiple groups of signal recognition models, Mel+VGG-FL proposed in this paper was verified as having a high recognition accuracy and certain recognition efficiency, performing the intelligent real-time recognition of key acoustic emission signals in the process of rock fracture more accurately, which can provide new ideas and methods for related research and the real-time intelligent recognition of rock fracture precursor characteristics.

An attention-based multi-scale convolution network for intelligent underwater acoustic signal recognition

Underwater acoustic signal identification is always an extremely challenging task due to the intricate marine environments and background noise interference. Existing techniques typically focus on handling only one type of specific features for recognition, overlooking a comprehensive representation of distinct vessel attributes and struggling to handle intricate scenarios. To address these challenges, we employ a band-pass filter bank for feature extraction. This process extracts multiple representations of the same data, each concentrating on distinct frequency components. Subsequently, the filtered multi-view representation is channeled into an attention-based multi-scale convolution network (AMSC-Net) for recognition purposes. AMSC-Net integrates a sequence of attention-based multi-scale convolution (AMSC) blocks to capture pivotal features tied to identifying diverse ship-radiated audio categories. Specifically, a set of parallel residual convolution layers with different kernels are designed to capture multi-scale features, and an adaptive channel attention module is followed to highlight dominant parts within global features. To enrich training data in terms of quantity and diversity, a data augmentation strategy is implemented during the training phase. Our proposals are evaluated on two real-world underwater acoustic signal datasets. Experimental results firmly establish the superiority of our proposed AMSC-Net, which substantially outperforms competitive DNN-based methods. Moreover, our proposals exhibit robustness across different noise levels, achieving an overall accuracy of 95.87% and 95.39% for the two datasets at 0 dB signal-to-noise ratio (SNR), respectively.

Research on Multi-Source Simultaneous Recognition Technology Based on Sagnac Fiber Optic Sound Sensing System

To solve the problem of multiple sound recognition in the application of Sagnac optical fiber acoustic sensing system, a multi-source synchronous recognition algorithm was proposed, which combined the VMD (variational modal decomposition) algorithm and MFCC (Mel-frequency cepstral coefficient algorithm) algorithm to pre-process the photoacoustic sensing signal, and uses BP neural network to recognize the photoacoustic sensing signal. The modal analysis and feature extraction theory of photoacoustic sensing signal based on the VMD and MFCC algorithms were presented. The signal recognition theory analysis and system recognition program design were completed based on the BP neural network. Signal acquisition of different sounds and verification experiments of the recognition system have been carried out in a laboratory environment based on the Sagnac fiber optic sound sensing system. The experimental results show that the proposed optical fiber acoustic sensing signal recognition algorithm has a simultaneous recognition rate better than 96.5% for six types of sounds, and the optical acoustic signal recognition takes less than 5.3 s, which has the capability of real-time sound detection and recognition, and provides the possibility of further application of the Sagnac-based optical fiber acoustic sensing system.

Theoretical and Experimental Study on Homologous Acoustic Emission Signal Recognition Based on Synchrosqueezed Wavelet Transform Coherence

The acoustic emission (AE) technique has been widely investigated for its ability to locate damage in structures. However, the selection of the arrival point of AE signals and the existence of nonhomologous AE signals can significantly affect the location accuracy of damages. The synchrosqueezed wavelet transform (SWT) was used in our previous research to pick the accurate arrival point, but the existence of the nonhomologous signals was neglected in the picking process. To address this limitation, the synchrosqueezed wavelet transform coherence (SWTC) method is proposed to improve the accuracy by recognizing homologous signals and suppressing the spectral leakage in this paper. Compared with the wavelet transform coherence (WTC) method previously used, the SWTC method using the squeezing wavelet coefficients obtained by the SWT can constitute a more explicit coherence graph of AE signals. This clear coherence graph can help reduce the effect of subjective factors in observing the coherence and improve the recognition accuracy of homologous signals. The effectiveness of the proposed method is experimentally verified on a steel pipe and a concrete beam. The results demonstrate that the SWTC accurately identifies homologous AE signals and effectively improves the localization accuracy across different signal densities, localization distances, and materials.

A light-weight neural network for marine acoustic signal recognition suitable for fiber-optic hydrophones

Fiber-optic hydrophones based on optical fiber sensing and communication technology have developed rapidly in recent years. With the advantages of high sensitivity, small size, and large dynamic range, they are easy to detect underwater acoustic signals, showing great potential in the field of marine ecological protection. Researchers can monitor the marine ecological environment by recognizing underwater optical fiber sensing signals collected by fiber-optic hydrophones in real-time. Deep learning models can achieve high recognition accuracy, but they usually have large amounts of parameters that makes them difficult to be deployed in mobile terminals of marine survey ships. To solve the key problem, this paper discussed the design of the Deep Light-Weight Attention Residual Network (DLA-ResNet). It introduces the depth-wise separable convolution (DSC) and the convolutional block attention module (CBAM) into ResNet18 to make its performance better. ResNet18 is a two-dimensional convolutional network (CNN) model applying residual blocks. Experiments verify that the accuracy of DLA-ResNet improved from ResNet18 is increased by about 2.04% and its size is reduced by 85.67%. The proposed scheme in this paper realizes an average recognition rate of 96.50% for eight types of underwater optical fiber sensing signals in the application of marine ecological environment monitoring. Moreover, its model size is only 6.12 MB and it only takes 27.90 ms, which is available to meet the needs of reliability and effectiveness.

Stacking Ensemble Learning for the Modulation Recognition of Underwater Acoustic Communication Signals

Effective detection and recognition of underwater acoustic communication signals is of great significance in the field of ocean exploration. In this paper, a novel deep-learning recognition method of modulation modes of underwater acoustic communication signals is proposed. The stacking ensemble learning framework is established on the basis of several single deep-learning classifiers with different network structures and parameter configurations. The proposed method achieved an average recognition accuracy rate of more than 95% on the dataset generated under a simulation environment. The performance is also verified by means of a comparison with the classic SVM method.

Modulation Recognition Method for Underwater Acoustic Communication Signals Based on Passive Time Reversal-Autoencoder with the Synchronous Signals.

In the process of the modulation recognition of underwater acoustic communication signals, the multipath effect seriously interferes with the signal characteristics, reducing modulation recognition accuracy. The existing methods passively improve the accuracy from the perspective of selecting appropriate signal features, lacking specialized preprocessing for suppressing multipath effects. So, the accuracy improvement of the designed modulation recognition models is limited, and the adaptability to environmental changes is poor. The method proposed in this paper actively utilizes common synchronous signals in underwater acoustic communication as detection signals to achieve passive time reversal without external signals and designs a passive time reversal-autoencoder to suppress multipath effects, enhance signals' features, and improve modulation recognition accuracy and environmental adaptability. Firstly, synchronous signals are identified and estimated. Subsequently, a passive time reversal-autoencoder is designed to enhance power spectrum and square spectrum features. Finally, a modulation classification is performed using a convolutional neural network. The model is trained in simulation channels generated by Bellhop and tested in actual channels which are different from the training period. The average recognition accuracy of the six modulated signals is improved by 10% compared to existing passive modulation recognition methods, indicating good environmental adaptability as well.

Hybrid feature extraction method of MFCC+GFCC helicopter noise based on wavelet decomposition

Aiming at the issue that the recognition accuracy of traditional acoustic signal features is low for helicopter acoustic signals with wind noise in the near field, a method of extracting mixed noise features of MFCC+GFCC based on wavelet decomposition is proposed. Firstly, the three-layer wavelet decomposition and reconstruction are applied to the helicopter acoustic signals; then, the Mel-Frequency Cepstral Coefficients (MFCC) and Gammatone-Frequency Cepstrum Coefficient (GFCC) are respectively extracted for the approximation and detail components; next, the coefficients of detail components which are averaged are combined with those of approximation components to form the hybrid feature parameters; finally, the convolutional neural network is used to classify the signal, to realize the correct recognition of helicopter acoustic signals. Experimental results show that the recognition accuracy is improved by almost 40% in contrast with other traditional methods, such as MFCC and GFCC, when the SNR is equal to -5dB. Further, When the SNR is -10dB, the recognition accuracy is more than 49%, while the traditional methods cannot effectively recognize the helicopter acoustic targets. The proposed feature extraction method can significantly improve the recognition accuracy in the low SNR environment, and provide a reference for near-field detection and recognition of helicopter acoustic targets.

PLDA in i-vector based underwater acoustic signals classification

ABSTRACT Intelligent recognition of underwater acoustic targets is crucial for exploiting marine resources. In this paper, we propose a deep learning method for underwater acoustic signal recognition consisting of three steps: Firstly, we extract the underwater acoustic signal's identification vector (i-vector) using a Gaussian mixture model. Secondly, we construct a probabilistic linear discriminant analysis (PLDA) model that divides the i-vector into two segments, Finally, we used a multi-long short-term memory neural network (MLSTMNN) to learn the features, outputting the underwater acoustic signal labels through the softmax layer. Experiments using the ShipsEar dataset can obtain a recognition rate of 84.8%; compared to the traditional Mel frequency Cepstral Coefficients (MFCC) and convolutional neural networks, the recognition rate greatly improved.

Acoustic Wake-Up Technology for Microsystems: A Review.

Microsystems with capabilities of acoustic signal perception and recognition are widely used in unattended monitoring applications. In order to realize long-term and large-scale monitoring, microsystems with ultra-low power consumption are always required. Acoustic wake-up is one of the solutions to effectively reduce the power consumption of microsystems, especially for monitoring sparse events. This paper presents a review of acoustic wake-up technologies for microsystems. Acoustic sensing, acoustic recognition, and system working mode switching are the basis for constructing acoustic wake-up microsystems. First, state-of-the-art MEMS acoustic transducers suitable for acoustic wake-up microsystems are investigated, including MEMS microphones, MEMS hydrophones, and MEMS acoustic switches. Acoustic transducers with low power consumption, high sensitivity, low noise, and small size are attributes needed by the acoustic wake-up microsystem. Next, acoustic features and acoustic classification algorithms for target and event recognition are studied and summarized. More acoustic features and more computation are generally required to achieve better recognition performance while consuming more power. After that, four different system wake-up architectures are summarized. Acoustic wake-up microsystems with absolutely zero power consumption in sleep mode can be realized in the architecture of zero-power recognition and zero-power sleep. Applications of acoustic wake-up microsystems are then elaborated, which are closely related to scientific research and our daily life. Finally, challenges and future research directions of acoustic wake-up microsystems are elaborated. With breakthroughs in software and hardware technologies, acoustic wake-up microsystems can be deployed for ultra-long-term and ultra-large-scale use in various fields, and play important roles in the Internet of Things.

Research on Electromagnetic Acoustic Emission Signal Recognition Based on Local Mean Decomposition and Least Squares Support Vector Machine

Research on Electromagnetic Acoustic Emission Signal Recognition Based on Local Mean Decomposition and Least Squares Support Vector Machine

A Novel Cross-Attention Fusion-Based Joint Training Framework for Robust Underwater Acoustic Signal Recognition

A Novel Cross-Attention Fusion-Based Joint Training Framework for Robust Underwater Acoustic Signal Recognition

Mutual mother-pup acoustic identification in Asian particolored bats.

In many vertebrates, vocal communication is crucial in parent-offspring interactions, and parents are often able to discriminate between the calls of their own and others' offspring. There are many reports on the unidirectional recognition of isolation calls of pups by maternal bats, but few studies on the ability of bat pups to recognize maternal acoustic signals. In this study, we investigated whether the echolocation pulses of female Asian particolored bats (Vespertilio sinensis) and isolation calls of pups differ statistically among individuals. We used two-choice playback experiments to test whether the mothers and pups of V.sinensis can recognize each other by acoustic signals. Both the echolocation pulses of mother bats and the isolation calls of pups contained sufficient individual characteristics. Playback experiments showed that mothers were able to recognize isolation calls of pups, and most pups greater than 12 days old were able to distinguish echolocation pulses of their own mother from those of other mothers. This is the first use of two-choice acoustic signal playback experiments to confirm that pups can recognize their mothers by echolocation calls. The results provide behavioral evidence for bidirectional recognition of acoustic signals between mothers and infants in frequency-modulated type bats.

Recognition of behavior state of Penaeus vannamei based on passive acoustic technology

Underwater signal processing based on passive acoustic technology has carried out a lot of research on the behavioral sound of underwater creatures and the protection of marine resources, which proves the feasibility of passive acoustic technology for long-term and non-destructive monitoring of underwater biological sound production. However, at present, most relevant research focuses on fish but little research on shrimp. At the same time, as the main economic breeding industry, Penaeus vannamei has a backward industrial structure, in which the level of intelligence needs to be improved. In this paper, the acoustic signals generated by different physiological behaviors of P. vannamei are collected based on passive acoustic technology. Their different behaviors are finally classified and identified through feature extraction and analysis. Meanwhile, the characteristic non-parametric ANOVA is carried out to explore the relationship between the acoustic signals and the behavior state of P. vannamei to achieve the purpose of real-time monitoring of the behavior state of P. vannamei. The experimental results show that linear prediction cepstrum coefficient (LPCC) and Mel-frequency cepstrum coefficient (MFCC) characteristic coefficients are effective in the classification and recognition of different behavioral acoustic signals with interspecific acoustic signals of P. vannamei. Meanwhile, the SVM classifier based on OvR classification strategy can model the acoustic signal characteristics of different underwater biological behaviors more efficiently and has classification accuracy as high as 93%.