2D DOA Estimation of Coherent Signals with a Separated Linear Acoustic Vector-Sensor Array

In this paper, a two-dimensional(2D) DOA estimation algorithm of coherent signals with a separated linear acoustic vector-sensor(AVS) array consisting of two sparse AVS arrays is proposed. Firstly,the partitioned spatial smoothing(PSS) technique is used to construct a block covariance matrix, so as to decorrelate the coherency of signals. Then a signal subspace can be obtained by singular value decomposition(SVD) of the covariance matrix. Using the signal subspace, two extended signal subspaces are constructed to compensate aperture loss caused by PSS.The elevation angles can be estimated by estimation of signal parameter via rotational invariance techniques(ESPRIT) algorithm. At last, the estimated elevation angles can be used to estimate automatically paired azimuth angles. Compared with some other ESPRIT algorithms, the proposed algorithm shows higher estimation accuracy, which can be proved through the simulation results.

Machine learning for parameters diagnosis of spark discharge by electro-acoustic signal

Discharge plasma parameter measurement is a key focus in low-temperature plasma research.Traditional diagnostics often require costly equipment,whereas electro-acoustic signals provide a rich,non-invasive,and less complex source of discharge information.This study harnesses machine learning to decode these signals.It establishes links between electro-acoustic signals and gas discharge parameters,such as power and distance,thus streamlining the prediction process.By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques,the Mel-Frequency Cepstral Coefficients(MFCCs)of the acoustic signals are extracted to construct the predictors.Three machine learning models(Linear Regression,k-Nearest Neighbors,and Random Forest)are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance.All models display impressive performance in prediction precision and fitting abilities.Among them,the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error(MSE=0.00571)and the highest R-squared value(R^(2)=0.93877).The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm,which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.

Acoustic emission signal identification of different rocks based on SE-1DCNN-BLSTM network model

In order to study fracture mechanism of rocks in different brittle mineral contents,this study pro-poses a method to identify the acoustic emission signal released by rock fracture under different brittle miner-al content(BMC),and then determine the content of brittle matter in rock.To understand related interference such as the noises in the acoustic emission signals released by the rock mass rupture,a 1DCNN-BLSTM network model with SE module is constructed in this study.The signal data is processed through the 1DCNN and BLSTM networks to fully extract the time-series correlation features of the signals,the non-correlated features of the local space and the weak periodicity law.Furthermore,the processed signals data is input into the fully connected layers.Finally,softmax function is used to accurately identify the acoustic emission signals released by different rocks,and then determine the content of brittle minerals contained in rocks.Through experimental comparison and analysis,1DCNN-BLSTM model embedded with SE module has good anti-noise performance,and the recognition accuracy can reach more than 90 percent,which is better than the traditional deep network models and provides a new way of thinking for rock acoustic emission re-search.

Acoustic location echo signal extraction of buried non-metallic pipelines based on EMD and wavelet threshold joint denoising

In the acoustic detection process of buried non-metallic pipelines,the echo signal is often interfered by a large amount of noise,which makes it extremely difficult to effectively extract useful signals.An denoising algorithm based on empirical mode decomposition(EMD)and wavelet thresholding was proposed.This method fully considered the nonlinear and non-stationary characteristics of the echo signal,making the denoising effect more significant.Its feasibility and effectiveness were verified through numerical simulation.When the input SNR(SNRin)is between-10 dB and 10 dB,the output SNR(SNRout)of the combined denoising algorithm increases by 12.0%-34.1%compared to the wavelet thresholding method and by 19.60%-56.8%compared to the EMD denoising method.Additionally,the RMSE of the combined denoising algorithm decreases by 18.1%-48.0%compared to the wavelet thresholding method and by 22.1%-48.8%compared to the EMD denoising method.These results indicated that this joint denoising algorithm could not only effectively reduce noise interference,but also significantly improve the positioning accuracy of acoustic detection.The research results could provide technical support for denoising the echo signals of buried non-metallic pipelines,which was conducive to improving the acoustic detection and positioning accuracy of underground non-metallic pipelines.

ESPRIT-Based Feature Extraction of Helicopter Acoustic Signal

Aim To extract harmonic frequencies of helicopter acoustic signal as features for hel icopter identification. Methods Estimation of signal parameters via rotational invariance techniques(ESPRIT) was selected to extract harmonic frequencies from really measured helicopter acoustic signal and an algorithm based on the SVD TLS was used. Results ESPRIT correctly extracted harmonic frequencies of helicopter using the data of limited length under the variousflight conditions. Conclusion ESPRIT is an effective method of extracting harmonic frequencies and using harmonic frequencies of helicopter acoustic signal to recognize helicopter is feasible.

Characteristics of acoustic emission signals in damp cracking coal rocks

A uniaxial load experiment on coal rocks at different stress rates was carried out, based on the characteristics of acoustic emission (AE) signals in cracking coal rocks, decomposition, de-noising and reconstruction for the AE signals through wavelet packet transform for solving the current problems created by the presence of noise in AE signals and the existing problems in AE signal processing. The results show that the various characteristics of AE signals in coal rocks cracking under different situations can be clearly reflected, after the AE signals are de-noised by the wavelet packet. Compared to dry coal rocks, the number of AE occurrences in damp coal rocks was significantly reduced, as well as the average amplitude. The number of AE occurrences in damp and dry coal rocks clearly increased with increases in the loading rate, but the largest amplitude of the AE signals in damp coal rocks has been reduced. There is no clear evidence of change in dry coal rocks.

Analysis of EMD Algorithm for Identification and Extraction of An Acoustic Signal in Underwater Channel Against Wind Driven Ambient Noise

Sonar generated acoustic signals transmitted in underwater channel for distant communications are affected by numerous factors like ambient noise, making them nonlinear and non-stationary in nature. In recent years, the application of Empirical Mode Decomposition(EMD) technique to analyze nonlinear and non-stationary signals has gained much attention. It is an empirical approach to decompose a signal into a set of oscillatory modes known as intrinsic mode functions(IMFs). In general, Hilbert transform is used in EMD for the identification of oscillatory signals. In this paper a new EMD algorithm is proposed using FFT to identify and extract the acoustic signals available in the underwater channel that are corrupted due to various ambient noises over a range of 100 Hz to 10 kHz in a shallow water region. Data for analysis are collected at a depth of 5 m and 10 m offshore Chennai at the Bay of Bengal. The algorithm is validated for different sets of known and unknown reference signals. It is observed that the proposed EMD algorithm identifies and extracts the reference signals against various ambient noises. Significant SNR improvement is also achieved for underwater acoustic signals.

A Survey on Underwater Acoustic Sensor Networks: Perspectives on Protocol Design for Signaling, MAC and Routing

Underwater acoustic sensor networks (UASNs) are often used for environmental and industrial sensing in undersea/ocean space, therefore, these networks are also named underwater wireless sensor networks (UWSNs). Underwater sensor networks are different from other sensor networks due to the acoustic channel used in their physical layer, thus we should discuss about the specific features of these underwater networks such as acoustic channel modeling and protocol design for different layers of open system interconnection (OSI) model. Each node of these networks as a sensor needs to exchange data with other nodes;however, complexity of the acoustic channel makes some challenges in practice, especially when we are designing the network protocols. Therefore based on the mentioned cases, we are going to review general issues of the design of an UASN in this paper. In this regard, we firstly describe the network architecture for a typical 3D UASN, then we review the characteristics of the acoustic channel and the corresponding challenges of it and finally, we discuss about the different layers e.g. MAC protocols, routing protocols, and signal processing for the application layer of UASNs.

Extraction of reflected waves from acoustic logging data using variation mode decomposition and curvelet transform

Remote reflection waves, essential for acquiring high-resolution images of geological structures beyond boreholes, often suffer contamination from strong direct mode waves propagating along the borehole.Consequently, the extraction of weak reflected waves becomes pivotal for optimizing migration image quality. This paper introduces a novel approach to extracting reflected waves by sequentially operating in the spatial frequency and curvelet domains. Using variation mode decomposition(VMD), single-channel spatial domain signals within the common offset gather are iteratively decomposed into high-wavenumber and low-wavenumber intrinsic mode functions(IMFs). The low-wavenumber IMF is then subtracted from the overall waveform to attenuate direct mode waves. Subsequently, the curvelet transform is employed to segregate upgoing and downgoing reflected waves within the filtered curvelet domain. As a result, direct mode waves are substantially suppressed, while the integrity of reflected waves is fully preserved. The efficacy of this approach is validated through processing synthetic and field data, underscoring its potential as a robust extraction technique.

Method of fuzzy uniformity analysis for simulation underwater acoustic signals

Since the simulation underwater acoustic signal is used in the semi-object simulation experiment of underwater weapons, it has great impression upon simulation fidelity. It is asked that whether simulation signals can replace the real signal effectually. Considering the randomness of signals, the interval estimation of feature parameters of simulation signals is made. By comparing the obtained confidence interval with the corresponding accept interval, the concept of similarity coefficient of simulation signals is given. By making a statistical analysis for similarity coefficient, the uniformity information of simulation signals is extracted, and the fuzzy number which expresses the fuzzy uniformity level of simu- lation signals is obtained. The analysis method on fuzzy uniformity of simulation underwater acoustic signals is presented. It is indi- cated by the application in simulation of target radiated-noises that the method is suitable and effectual for the simulation research on underwater acoustic signals, and the analysis result may provide support for decision-making relative to perfecting simulation sys- tems and applying simulation signals.

An improved joint method for onset picking of acoustic emission signals with noise

The onset times of acoustic signals with spikes,heavy bodies and unclear takeoffs are difficult to be picked accurately by the automatic method at present.To deal with this problem,an improved joint method based on the discrete wavelet transform(DWT),modified energy ratio(MER)and Akaike information criterion(AIC)pickers,has been proposed in this study.First,the DWT is used to decompose the signal into various components.Then,the joint application of MER and AIC pickers is carried out to pick the initial onset times of all selected components,where the minimum AIC position ahead of MER onset time is regarded as the initial onset time.Last,the average for initial onset times of all selected components is calculated as the final onset time of this signal.This improved joint method is tested and validated by the acoustic signals with different signal to noise ratios(SNRs)and waveforms.The results show that the improved joint method is not affected by the variations of SNR,and the onset times picked by this method are always accurate in different SNRs.Moreover,the onset times of all acoustic signals with spikes,heavy bodies and unclear takeoffs can be accurately picked by the improved joint method.Compared to some other methods including MER,AIC,DWT-MER and DWT-AIC,the improved joint method has better SNR stabilities and waveform adaptabilities.

Identification of acoustic emission signal in aluminum alloys spot welding based on fractal theory

The acoustic emission signal of aluminum alloys spot welding includes the information of forming nugget and is one of the important parameters in the quality control. Due to the nonlinearity of the signals, classic Euclidean geometry can not be applied to depict exactly. The fractal theory is implemented to quantitatively describe the characteristics of the acoustic emission signals. The experiment and calculation results show that the box counting dimension of acoustic emission signal, between 1 and 2, are distinctive from different nugget areas in AC spot welding. It is proved that box counting dimension is an effective characteristic parameter to evaluate spot welding quality. In addition, fractal theory can also be applied in other spot welding parameters, such as voltage, current, electrode force and so on, for the purpose of recognizing the spot welding quality.

Leak Detection of Gas Pipelines Based on Characteristics of Acoustic Leakage and Interfering Signals

When acoustic method is used in leak detection for natural gas pipelines,the external interferences including operation of compressor and valve,pipeline knocking,etc.,should be distinguished with acoustic leakage signals to improve the accuracy and reduce false alarms.In this paper,the technologies of extracting characteristics of acoustic signals were summarized.The acoustic leakage signals and interfering signals were measured by experiments and the characteristics of time-domain,frequency-domain and time-frequency domain were extracted.The main characteristics of time-domain are mean value,root mean square value,kurtosis,skewness and correlation function,etc.The features in frequency domain were obtained by frequency spectrum analysis and power spectrum density,while time-frequency analysis was accomplished by short time Fourier transform.The results show that the external interferences can be removed effectively by the characteristics of time domain,frequency domain and time-frequency domain.It can be drawn that the acoustic leak detection method can be applied to natural gas pipelines and the characteristics can help reduce false alarms and missing alarms.

Underwater acoustic signal extraction algorithm in a strong noise environment

In the exploration,tracking and positioning of underwater targets,it is necessary to perform frequency domain analysis and correlation calculation on the underwater acoustic signals of the target radiation.In a strong noise environment,the target signal may be overwhelmed by noise,resulting in an inability to effectively identify the target.Aiming at this problem,this paper presents a method of signal-noise separation by combining Fourier denoising with wavelet transform to realize underwater acoustic signal extraction in a strong noise environment.The combination algorithm of Fourier coefficient threshold adjustment and wavelet threshold transform is designed,and performance of the algorithm is tested.Simulation results show that the combination algorithm can effectively extract underwater acoustic signals when signal-to-noise ratio(SNR)is-15 dB,which can improve the SNR to 8.2 dB.

Line Spectrum Enhancement of Underwater Acoustic Signals Using Kalman Filter

To detect weak underwater acoustic signals radiated by submarines and other underwater equipment,an effective line spectrum enhancement algorithm based on Kalman filter and FFT processing is proposed.The proposed algorithm first determines the frequency components of the weak underwater signal and then filters the signal to enhance the line spectrum,thereby improving the signal-to-noise ratio(SNR).This paper discussed two cases:one is a simulated signal consisting of a dual-frequency sinusoidal periodic signal and Gaussian white noise,and the signal is received after passing through a Rayleigh fading channel;the other is a ship signal recorded from the South China Sea.The results show that the line spectrum of the underwater acoustic signal could be effectively enhanced in both cases,and the filtered waveform is smoother.The analysis of simulated signals and ship signal reflects the effectiveness of the proposed algorithm.

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.

Application of Mellin Transform in Wideband Underwater Acoustic Signal Processing

According to the features of the wideband underwater acoustic signals,an algorithm for the wideband ambiguity function is put forward based on Mellin transform.The wideband acoustic signal processing using the fast Mellin transform is also explored.The theoretical analysis and simulation results show that the algorithm has not only high computation efficiency but also good concentration in wideband ambiguity domain.It suits for the wideband underwater acoustic signal processing.

Measuring Acoustic Wave Transit Time in Furnace Based on Active Acoustic Source Signal

Accurate measurement of transit time for acoustic wave between two sensors installed on two sides of a furnace is a key to implementing the temperature field measurement technique based on acoustical method. A new method for measuring transit time of acoustic wave based on active acoustic source signal is proposed in this paper, which includes the followings： the time when the acoustic source signal arrives at the two sensors is measured first; then, the difference of two arriving time arguments is computed, thereby we get the transit time of the acoustic wave between two sensors installed on the two sides of the furnace. Avoiding the restriction on acoustic source signal and background noise, the new method can get the transit time of acoustic wave with higher precision and stronger ability of resisting noise interference.

Chaos analysis of acoustic emission signals in spot welding process

Based on chaos time series and fractal theory, acoustic emission signals were studied in the process of spot welding. According to calculating 8 welding parameters using phase space reconstruction method, the largest Lyapunov exponents were positive values and chaos characteristics were firstly discovered from acoustic emission signals in spot welding. In order to evaluate acoustic emission signal, Hausdorff dimension is put forward to analyze and estimate chaos characteristics. The experiment and calculation results indicate that the Hausdorff dimension of acoustic emission signal is significantly distinguishable in the nuggets with different welding parameters. This research provides a new method for measuring the resistance spot welding quality.

Some Aspects of Analysis of Dolphins’ Acoustical Signals

Dolphins produce various types of sounds in a wide range of frequencies. Characteristics of some sounds till now have not been correctly registered, that influenced on interpretation of their functions. Studying of the characteristics and functions of dolphins’ acoustical signals is the purpose of the present work. In this work the acoustical signals of two dolphins (Tursiops truncatus) were registered by two-channel system in the frequencies band up to 200 kHz at quasi-stationary position of the dolphins. The dolphins along with whistles are producing the packs of coherent and non-coherent broadband pulses. The waveform and spectrum of coherent pulses was invariable within a pack, but considerably varies from a pack to a pack. The waveform of each non-coherent pulse vary from a pulse to a pulse in each pack, therefore their spectrum also vary from a pulse to a pulse and have many extremums in the band of 6 - 200 kHz. It is very likely that the non-coherent pulses play a part of phonemes of a dolphin spoken language and the probing signals of dolphin’s non- coherent sonar. The use possibility of the signals by dolphins for communication and orientation was considered, as the signals apparently are bimodal. Results of the work have significance for further studying of the dolphin’s sonar and spoken language.