Underwater acoustic signal denoising model based on secondary variational mode decomposition

Due to the complexity of marine environment,underwater acoustic signal will be affected by complex background noise during transmission.Underwater acoustic signal denoising is always a difficult problem in underwater acoustic signal processing.To obtain a better denoising effect,a new denoising method of underwater acoustic signal based on optimized variational mode decomposition by black widow optimization algorithm(BVMD),fluctuation-based dispersion entropy threshold improved by Otsu method(OFDE),cosine similarity stationary threshold(CSST),BVMD,fluctuation-based dispersion entropy(FDE),named BVMD-OFDE-CSST-BVMD-FDE,is proposed.In the first place,decompose the original signal into a series of intrinsic mode functions(IMFs)by BVMD.Afterwards,distinguish pure IMFs,mixed IMFs and noise IMFs by OFDE and CSST,and reconstruct pure IMFs and mixed IMFs to obtain primary denoised signal.In the end,decompose primary denoising signal into IMFs by BVMD again,use the FDE value to distinguish noise IMFs and pure IMFs,and reconstruct pure IMFs to obtain the final denoised signal.The proposed mothod has three advantages:(i)BVMD can adaptively select the decomposition layer and penalty factor of VMD.(ii)FDE and CS are used as double criteria to distinguish noise IMFs from useful IMFs,and Otsu algorithm and CSST algorithm can effectively avoid the error caused by manually selecting thresholds.(iii)Secondary decomposition can make up for the deficiency of primary decomposition and further remove a small amount of noise.The chaotic signal and real ship signal are denoised.The experiment result shows that the proposed method can effectively denoise.It improves the denoising effect after primary decomposition,and has good practical value.

Study on characteristics of acoustic signals generated by different DC discharge modes

Acoustic signals contain rich discharge information.In this study,the acoustic signal characteristics of transient glow,spark,and glow discharges generated through DC pin–pin discharge were investigated.The signals were analyzed in the time,frequency,and time–frequency domains,and the correlation between the electric and the acoustic signal was studied statistically.The results show that glow discharge does not produce measurable sound signals.For the other modes,with a decrease in the discharge gap,the amplitude of the acoustic signal increases sharply with mode transformation,the short-time average energy becomes higher,and the frequency components are more abundant.Meanwhile,the current pulse and sound pressure pulse have a one-to-one relationship in the transient glow and spark regimes,and they are positively correlated in amplitude.A brief theoretical analysis of the mechanism of plasma sound and the trends of signals in different modes is presented.Essentially,the change in the discharge energy is closely related to the sound generation of the plasma.

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.

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.

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.

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.

Detecting the Nonlinearity of Fish Acoustic Signals

This paper discusses the nonlinearity of fish acoustic signals by using the surrogate data method. We compare the difference of three test statistics - time-irreversibility Trey, correlation dimension D2 and auto mutual information function I between the original data and the surrogate data. We come to the conclusion that there exists nonlinearity in the fish acoustic signals and there exist deterministic nonlinear components; therefore nonlinear dynamic theory can be used to analyze fish acoustic signals.

Extraction of Partial Discharge Acoustic Signal by Wavelet Transform with Teager′s Energy Operator

To develop a measurement system for monitoring partial discharge （PD） without the effect of external interferences,an algorithm of PD signal extraction based on wavelet transform with Teager＇s energy operators was presented. Acoustic signal generated by PD was selected to remove excessive interfering signals and electromagnetic interferences. Acoustic signals were collected and decomposed into I0 levels by wavelet transform into approximation and detail components. “Daubechies 25” was proved to be the most suitable mother wavelet for the extraction of PD acoustic signals. Compared with conventional wavelet denoising method, Teager＇s energy operators were adopted to the PD signal reconstruction and the signal to noise ratio was in creased by 20%-25% inthe experiment,without lost in energy and pulse amplitude.

A Design of Acoustic Signal Wireless Acquisition System for Stored Grain Pests Based on Compressed Sensing

In order to achieve the acoustic signal distributed acquisition of stored grain pests, a novel acoustic signal acquisition system was presented based on the wireless sensor networks. And the system architecture, hardware configuration, and software were introduced in detail. Considering bandwidth limitation of wireless sensor networks, random sampling algorithm based on the compressed sensing theory was proposed. The developed acoustic signal acquisition system was applied in sampling the crawl acoustic signal of Tribolinm castaneum Herbst adults in granary. Preliminary experimentation indicated the rationality and practicability of the developed system and the proposed algorithm. They can implement the remote, real-time, and reliable wireless transmission for the acoustic signal sampled data of multiple points stored grain pests effectively.

Classifying ships by their acoustic signals with a cross-bispectrum algorithm and a radial basis function neural network

An algorithm for estimating the cross-bispectrum of an acoustic vector signal was formulated. Composed features of sound pressure and acoustic vector signals are extracted by the proposed algorithm and other estimating algorithms for secondary and higher order spectra. Its effectiveness was tested with lake and sea trial data. These features can be used to construct an input vector set for a radial basis function neural network. The classification of vessels can then be made based on the extracted features. It was shown that the composed features of acoustic vector signals are more easily divided into categories than those of pressure signals. When using the composed features of acoustic vector signals, the recognition rate of underwater acoustic targets improves.

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.

A feedback control method for phase signal demodulation in fber-optic hydrophones

In the realm of acoustic signal detection,the identification of weak signals,particularly in the presence of negative signal-to-noise ratios,poses a significant challenge.This challenge is further heightened when signals are acquired through fiber-optic hydrophones,as these signals often lack physical significance and resist clear systematic modeling.Conventional processing methods,e.g.,low-pass filter(LPF),require a thorough understanding of the effective signal bandwidth for noise reduction,and may introduce undesirable time lags.This paper introduces an innovative feedback control method with dual Kalman filters for the demodulation of phase signals with noises in fiber-optic hydrophones.A mathematical model of the closed-loop system is established to guide the design of the feedback control,aiming to achieve a balance with the input phase signal.The dual Kalman filters are instrumental in mitigating the effects of signal noise,observation noise,and control execution noise,thereby enabling precise estimation for the input phase signals.The effectiveness of this feedback control method is demonstrated through examples,showcasing the restoration of low-noise signals,negative signal-to-noise ratio signals,and multi-frequency signals.This research contributes to the technical advancement of high-performance devices,including fiber-optic hydrophones and phase-locked amplifiers.

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.

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.

Physical contact, volatiles, and acoustic signals contribute to monogamy in an invasive aggregating bark beetle

The behavioral strategies and mechanisms by which some insects maintain monogamous mating systems are not well understood.We investigated the mating system of the bark beetle Dendroctonus valens,and identified several contributing mechanisms.Field and laboratory observations suggest the adults commonly form permanent bonds during host colonization.Moreover,it showed mated females that remained paired with males produced more offspring than mated females that were alone in galleries.In bioassays,a second female commonly entered a gallery constructed by a prior female.Videos show she commonly reached the location of the first female,but they did not engage in actual fighting.Rather,the second female typically departs to form her own gallery.Acoustic signaling likewise does not appear to influence female-female encounters,based on controlled muting experiments.Instead the intruder appears to perceive the residents presence by physical contact.Both acoustic signals and volatiles released by females during gallery constructing were shown to attract males.After a male joined a female in a gallery,the male-produced aggressive sounds,which were shown by playback to deter other males from entering the gallery.Unlike female-female interactions,resident males use their head and rear to push intruders out of galleries.Additionally,volatiles released by males during feeding repelled intruding males,discouraging them from entering the gallery.Males also construct plugs that block the entrance,which may prevent subsequent males and predators from entering the gallery.Thus,D.valens has evolved multifaceted mechanisms contributing to single pairings that confer benefits to both sexes.

Synthesized acoustic signal characteristics of net-cage-cultured Large yellow croaker,Pseudosciaena crocea

This study performs the quantitative analysis and comparison to acoustic signal characteristics of Large yellow croaker （Pseudosciaena crocea） at two different ages. The sounds were recorded from the fishes in a net-cage. Two exponential oscillation functions are built to fit the acoustic signal of the fishes. The signal characteristic of the oscillation frequency and attenuation coefficient was described quantitatively. Simulation curves of the function could fit well acoustic signals. Both the average oscillation frequency and attenuation coefficient of the fitted signals from the 13-15-month-old fishes are lower than those from the 7-8-month-old fishes. The results suggest that the oscillation frdquency and attenuation coefficient of the acoustic signal flmction may be relevant to the physical process of sound production and age characteristics of Large yellow croaker. This study may be valuable for the acoustic application to the artificial culture of the species.

Acoustic signal analysis of underwater elastic cylinder

The echoes of underwater elastic cylinder comprise two types of acoustic components: Geometrical scattering waves and elastic scattering waves. The transfer function is appropriate to characterize the echo of targets. And the discrete wavelet transform of amplitude spectrum is presented and used to identify the resonant components of underwater targets.

Fish as a source of acoustic signal measurement in an aquaculture tank:Acoustic sensor based time frequency analysis

Acoustic signals travels rapidly in water without attenuating fish telemetry.The digital sonar and passive acoustic has been used for fish monitoring and fish feeding.However,it is an urgent need to introduce new techniques in order to monitor the growth rate of fish during harvesting and without causing adverse effects to the harvested fish.Therefore,a novel technique was introduced to probe the acoustic signal frequency ratio in absence and presence of the fish in tanks,which basically uses an acoustic sensor(hydrophone),acoustic signal processing system(scope meter),and a signal monitoring system(fluke view).Acoustic signals were selected from 48-52 Hz frequency,measure of dispersion of frequency signal represented as a function of time via Xlstat software.Measure of dispersion displayed a significant effect of acoustic signal in the presence and absence of the fish in tanks.These optimised protocols of this study will help to control and prevent excessive wastage of feed and enhance proper utilization of feed that chiefly enhance fish growth in aquaculture.

Automatic depression recognition by intelligent speech signal processing:A systematic survey

Depression has become one of the most common mental illnesses in the world.For better prediction and diagnosis,methods of automatic depression recognition based on speech signal are constantly proposed and updated,with a transition from the early traditional methods based on hand‐crafted features to the application of architectures of deep learning.This paper systematically and precisely outlines the most prominent and up‐to‐date research of automatic depression recognition by intelligent speech signal processing so far.Furthermore,methods for acoustic feature extraction,algorithms for classification and regression,as well as end to end deep models are investigated and analysed.Finally,general trends are summarised and key unresolved issues are identified to be considered in future studies of automatic speech depression recognition.