Energy Distribution Of Noise Research Articles

Comparison of impulse noise energy distribution from two generations of parachute gun

The rocket ejection seat is an advanced life-saving device for fighter pilots. However, it can also produce damaging impulse noise during ejection, which could compromise the pilot’s hearing health, and the manufacturer must assess the noise damage. This study uses Variational Mode Decomposition (VMD) and Hilbert-Huang transform (HHT) to analyze energy-instantaneous distribution over time and frequency-instantaneous and energy contribution of different modes of impulse noise generated by the parachute gun in two generations of rocket ejection seats. The results demonstrate considerable disparities in the energy distribution of impulse noise generated by the parachute gun in the new generation seat compared to the old generation seat. The new generation seat has higher overall energy, more concentrated action time, and a higher energy contribution in the 1-10 kHz frequency range. These findings provide valuable data for the design of the new generation of noise protection equipment for pilots.

A Multi-Frame Superposition Detection Method for Dim-Weak Point Targets Based on Optimized Clustering Algorithm

Long-range surveillance and early warning of space targets are significant factors in space security. Under remote observation conditions, the energy performance of the target is weak and subject to environmental and imaging process contamination. Most detection methods are aimed at targets with a high signal-to-noise ratio (SNR) or local contrast, and the detection performance for dim-weak small targets is poor; therefore, the target signal is often enhanced by energy accumulation. However, owing to the interference caused by the instability of the imaging system, energy accumulation errors occur in the target, resulting in the dispersion of the target energy, making detection a challenge. To solve the above problem, this study proposed a multi-frame superposition detection method for dim-weak point targets based on an optimized clustering algorithm by combining the clustering method with the inherent features of the target and using the difference between the target and noise energy distribution for detection. First, we simulated the multi-frame imaging process of the target post-disturbance and established an optical imaging system model of the dim-weak target. Subsequently, we used data dimension reduction and outlier removal to extract the target potential area. Finally, the data were sent to the clustering model for calculation and judgment. Given that the accuracy rate reaches 87.1% when the SNR is 1 dB, the experimental results show that the detection method proposed in this paper can effectively detect dim-weak targets with low SNR. In addition, there is a significant improvement in the detection performance of the receiver characteristic curve compared with other algorithms in the real scene, which further proves the superiority of the method in this paper.

Numerical study on the aerodynamic and acoustic scale effects for high-speed train body and pantograph

Based on computational fluid dynamics (CFD) and Lighthill acoustic analogy theory, the aerodynamics and acoustic scale effects of high-speed train bodies and pantographs are numerically studied, by changing the model scale. The model scales are 1/1, 1/2, 1/4 and 1/8 respectively. The aerodynamic force, flow field behavior, surface pressure and far-field noise of the train body and pantograph models, at different model scales are compared and analyzed. The numerical simulation is in good agreement with the experiment. The reliability of the numerical calculation method has been proven. The results show that as the scale of the model decreases, the root mean square (RMS) value and the degree of fluctuation of the aerodynamic drag coefficient of the pantograph increase. In addition, the aerodynamic drag coefficient of the train body increases, but the degree of fluctuation decreases. The similarity of the unsteady flow field of the pantograph at different model scales is strong, but the similarity of the train body is weak. The main frequency and power spectral density distribution of the pulsating pressure on the surface of the pantograph have obvious corresponding relationships with the model scale, but the train body has no obvious regularity. The longitudinal and lateral propagation characteristics of aerodynamic noise from the train body and pantograph are not affected by the model scale, but the sound pressure level changes. Compared with the train body, the far-field aerodynamic noise of the pantograph is less affected by the model scale. The energy distribution of noise is also affected by the scale of the model. As the model scale decreases, the energy concentration range of radiated noise will change from low frequency to medium high frequency.

Spatial autocorrelation method for reliable measurements of two-station dispersion curves in heterogeneous ambient noise wavefields

SUMMARYThe microtremor survey method (MSM) is used to estimate S-wave velocity profiles from microtremors or ambient noise. Although array-based MSM analyses are usually used for shallow exploration purposes because of their robustness, the extraction of numerous phase-velocity dispersion curves by two-station microtremor analysis is attractive because those dispersion curves can be used to construct high-resolution phase-velocity maps by solving a least-squares problem. However, in exploration studies (>1 Hz), the reliability of two-station microtremor analysis can be affected by short data acquisition times and heterogeneous noise distributions mainly caused by anthropogenic noises. In this study, we propose a new approach to estimate surface wave dispersion curves between station pairs considering a heterogeneous ambient noise distribution based on the spatial autocorrelation method. We first estimated azimuthal variations of noise energy from the complex coherencies between all station pairs in a receiver array, and then estimated dispersion curves between station pairs. Our field example demonstrates that modelling the azimuthal noise energy distribution allows us to use not only the real parts of complex coherencies, but also the imaginary parts, which are usually neglected when assuming a homogeneous noise field. The simultaneous use of the real and imaginary parts of complex coherencies improves the reliability and continuity of phase-velocity estimations between station pairs. Because the stability of phase-velocity estimations depends on the azimuths between station pairs, we carefully selected between-station azimuths that produce stable phase velocities. Selected phase velocities at 8 Hz can be used to construct high-resolution phase-velocity maps with least-squares inversion. Because our approach does not require a regular receiver interval for two-station analysis, it allows for more flexible seismic array geometries. This is particularly important for MSM analyses in urban areas, where limited space is available to install seismic stations. We conclude that our proposed approach is effective in reconstructing high-resolution shallow structures in heterogeneous ambient noise fields.

On denoising satellite altimeter measurements for high-resolution geophysical signal analysis

Abstract Satellite radar altimeter observations are key to advanced studies in ocean dynamics, particularly those focusing on mesoscale processes. To resolve scales below about 100 km, because altimeter measurements are often characterized by a low signal-to-noise ratio (SNR), low-pass filtering or least-squares curve fitting is generally applied to smooth the data before analysis. Here, we present an alternative method. It is based on Empirical Mode Decomposition (EMD) developed to analyze non-stationary and non-linear processes, which adaptively projects a signal on a basis of empirical AM/FM functions called Intrinsic Modulation Functions (IMFs). Applied to a Gaussian noise signal, the EMD provides a set of IMFs with a predictable distribution of noise energy that can be exploited by wavelet-inspired threshold methods to provide an efficient approach to data denoising. The EMD method performs a local analysis of the SNR, does not require a priori assumptions about the underlying geophysical signal, e.g., its degree of smoothness or its compliance with a particular mathematical framework. The signal is simply assumed to be the sum of a piecewise-smooth deterministic part and a stochastic part. The proposed EMD-based denoising approach is therefore well suited for mapping non-linear features, such as strong gradients, and extreme values without significant smoothing. Using Jason-2, Cryosat-2, and Saral/AltiKa significant wave height measurements, the method provides an effective means of mapping overlooked geophysical variability of sea state at scales between about 100 km and 25 km, a range largely impacted by low SNR. Below 25 km, a spectral hump caused by inhomogeneities in the altimeter footprint essentially dominates the signal. In addition, the EMD method provides a consistent approach for long-term noise analysis and monitoring under global and local conditions. The proposed method is a step forward that will enable better exploitation of the unique set of altimeter observations that now covers more than 25 years.

Image denoising by low‐rank approximation with estimation of noise energy distribution in SVD domain

Low-rank approximation has shown great potential in various image tasks. It is found that there is a specific functional relationship about singular values between the original image and a series of noisy images, which can be used to construct the singular values of a noise-free image. In this study, the authors propose a novel denoising method based on the above facts and low-rank approximation theory. Firstly, they estimate the noise energy distribution of the group matrix in the singular value decomposition (SVD) domain using the energy characteristics of the image with different noise levels. The energy distribution of the noise is shrunk to obtain the energy distribution of the true signal. Then, based on the optimal energy compaction property of SVD, the low-rank property of matrix is constrained in the SVD domain to obtain the low-rank approximation of the matrix. Moreover, an iterative back projection method is adopted in this study to suppress residual noise. A new noise standard deviation estimation approach, targeted at the back projection process, is proposed to effectively optimise the denoising results during the iteration. Experimental results show that the authors’ method efficiently decreases the noise and achieves comparable denoising performance to the state-of-the-art methods regarding both quantitative measurement and visual effect.

Estimation of incident energy angular distribution at the building envelope in urban environment

This paper presents preliminary results of a research dedicated to noise analysis at the building envelope using microphone arrays. Motif for such a research is a dependence of facade elements insulation characteristics on incident angles of noise impeding a facade. The goal is to determine the influence of different building and street configurations on the discrepancies between laboratory and in-situ values of facade elements sound insulation, and develop a model for predicting in-situ facade performances depending on building surroundings. Microphone array mounted on a facade can provide an insight into impeding noise energy distribution over angles of arrival. This distribution is examined for a number of different scenarios, including buildings in canyon and non-canyon streets, different mounting heights of the array, different widths of streets etc. Planar array consisting of 24 microphones with a diameter of 2.2 m is used for measurements. Results for given scenarios are presented and compared with...

A hybrid spatial-spectral denoising method for infrared hyperspectral images using 2DPCA

The traditional noise reduction methods for 3-D infrared hyperspectral images typically operate independently in either the spatial or spectral domain, and such methods overlook the relationship between the two domains. To address this issue, we propose a hybrid spatial-spectral method in this paper to link both domains. First, principal component analysis and bivariate wavelet shrinkage are performed in the 2-D spatial domain. Second, 2-D principal component analysis transformation is conducted in the 1-D spectral domain to separate the basic components from detail ones. The energy distribution of noise is unaffected by orthogonal transformation; therefore, the signal-to-noise ratio of each component is used as a criterion to determine whether a component should be protected from over-denoising or denoised with certain 1-D denoising methods. This study implements the 1-D wavelet shrinking threshold method based on Stein’s unbiased risk estimator, and the quantitative results on publicly available datasets demonstrate that our method can improve denoising performance more effectively than other state-of-the-art methods can.

A speech enhancement approach based on noise classification

For speech enhancement, most existing approaches do not consider the differences, between various types of noise, which significantly affect the performance of speech enhancement. In this paper, we propose a novel speech enhancement approach by taking into account the different characteristic statistical properties of various noise on the basis of noise classification. To classify noise, an effective noise classification method is firstly developed by exploiting the features of noise energy distribution in the Bark domain. Then, based on the noise types, the speech enhancement approach is obtained by forming the optimal parameter combinations for the optimally modified log-spectral amplitude (OM-LSA) speech estimator with the improved minima controlled recursive averaging (IMCRA) noise estimator, where the parameter combinations consisting of the smoothing parameters for smoothing the noisy power spectrum and the recursive averaging in the noise spectrum estimation as well as the weighting factor for the a priori SNR estimation, are built through the enhancement of noisy speech samples. Finally, extensive experiments are carried out in terms of objective evaluation under various noise conditions, and the experimental results show that the proposed approach yields better performance compared with the conventional OM-LSA with IMCRA in speech enhancement.

Characterisation of impulse noise effects on space-time block-coded orthogonal frequency division multiplexing (OFDM) signal reception

This correspondence presents the characterisation of impulse noise effects on the space-time block-coded orthogonal-frequency-division-multiplexing (STBC-OFDM) symbol reception, in which the conventional OFDM system is combined with the antenna-diversity/spatial-diversity in order to achieve the high data transmission rates. The weak impulse noise may be combated by the longer OFDM symbol duration, in which the total energy of noise spreads among the simultaneously transmitted OFDM sub-carriers irrespective of the impulse noise energy distribution. Specifically, the focus of the presented work is on “noise bucket” effect, in which the STBC-OFDM system is considered to be working in the impulsive environment. It is demonstrated by simulation results that for the occurrence of a significant number of impulses per symbol duration, the noise distribution at the input of the receiver’s final decision device is approximately Gaussian. Therefore for simulation purpose, the impulsive environment may be generated by the mere incorporation of equivalent Gaussian noise. Besides, some simulation results are presented using 16-QAM / STBC-OFDM system to give some insight into the symbol error rate variations with respect to the different number of sub-carriers and characteristics of impulsive environment. Key words: Impulse noise, space-time block-coded (STBC), orthogonal-frequency-division-multiplexing, Gaussian distribution, i nverse discrete Fourier transform (IDFT), M-ary QAM.

Analysis of ambient noise energy distribution and phase velocity bias in ambient noise tomography, with application to SE Tibet

SUMMARY Green’s functions (GFs) of surface wave propagation between two receivers can be estimated from the cross-correlation of ambient noise under the assumption of diffuse wavefields or energy equipartitioning. Interferometric GF reconstruction is generally incomplete, however, because the distribution of noise sources is neither isotropic nor stationary and the wavefields considered in the cross-correlation are generally non-diffuse. Furthermore, medium complexity can affect the empirical Green’s function (EGF) from the cross-correlation if noise sources are all far away (i.e. approximately plane-wave sources), which makes the problem non-linear. We analyse the effect of uneven ambient noise distribution and medium heterogeneity and azimuthal anisotropy on phase velocities measured from EGFs with an asymptotic plane wave (far-field) approximation (which underlies most constructions of phase velocity maps). Phase velocity bias due to uneven noise distribution can be determined (and corrected) if the noise energy distribution and the velocity model are known. We estimate the (normalized) azimuthal distribution of ambient noise energy directly from the cross-correlation functions obtained through ambient noise interferometry. The (smaller, second order) bias due to non-linearity can be reduced iteratively, for instance by using the tomographic model that results from the inversion of uncorrected data. We illustrate our method for noise energy estimation, phase velocity bias suppression, and ambient noise tomography (including azimuthal anisotropy) with data from a seismic array (26 stations) in SE Tibet. We show that the phase velocity bias due to uneven noise energy distribution (and medium complexity) in SE Tibet has a small effect (<1 per cent) on the isotropic part phase velocities (for T = 10–30 s) and the azimuthal anisotropy obtained before and after bias correction shows very similar pattern and magnitude.

Robust voice activity detection based on noise eigenspace

In this study, we propose a voice activity detector (VAD) based on a noise eigenspace. which improve the robustness of VAD by utilizing the compression capability of the eigenspace. A noise eigenspace is constructed by.using eigenvalue decomposition of the noise correlation matrix. When noisy speech is projected into the noise eigenspace, the noise energy is packed into a few dimensions with large eigenvalues, and those dimensions hopefully possess relatively less speech. because the speech energy distribution is usually different from noise energy distribution. The noise can be reduced by discarding those dimensions with large noise energy, while no significant loss occurs in speech. To track noise variation, the noise eigenspace is periodically updated, where the computation cost for eigenspace construction can be kept at an acceptable level. The proposed VAD was evaluated using the TIMIT database mixed with several noises. The experiment showed that the proposed VAD is more accurate than previous VADs in noisy environments.

Initial study of ambient noise modeling for 2001 ASIAEX SCS Experiment

Although ambient noise has been an object of study for a long time, there is little modeling done for real ocean environment to reflect oceanographic changes. The numerical simulation in this study is based on PE code, with environmental and bathymetry data from the 2001 ASIAEX SCS experiment. Simple sources were distributed near sea surface in the model, and source levels in significant range were estimated by the wave height data of experiment. It is well known that strong internal waves often occur in the South China Sea, which can cause significant change in water temperature distribution, thus the sound propagation. Moreover, the roughness of sea surface and bottom are also important factors in noise energy distribution. In this initial study, the fluctuation of noise energy would be evaluated by the varying spatial ocean environment. The computational noise levels would be compared with measurements on vertical line array in ASIAEX SCS experiment for adjusting the parameters and source levels.