Stable Noise Environment Research Articles

Comprehensive evaluation on wearing and hearing comfort of over-ear headphones based on adjustable clamping force

Headphones are wearable devices that directly contact the human body. Ergonomics focuses on the wearing comfort associated with clamping force, which is closely related to the user's tactile perception. Additionally, as auditory input terminal devices, headphones should also encompass the aspect of hearing comfort. Theoretically, slight and uneven clamping force may cause noise exposure, subsequently influencing hearing comfort. Conversely, an overloaded clamping force, while aiming to improve hearing conditions, may introduce discomfort for the wearer. Therefore, the current work concerns the interplay between wearing and hearing comfort. In this study, four loudspeakers placed around a listener in a sound insulation room render a stable noise environment. A dynamometer mounted on a horizontal track radially supplies specific clamping force on the two earmuffs of an over-ear headphone worn by the listener. Simultaneously, a comfort questionnaire was employed to investigate users' subjective perceptions of wearing and hearing comfort under various clamping forces. These results could provide a valuable reference for the design and evaluation of over-ear headphones.

CML based parameter estimation of FH signals in alpha stable noise environment

CML based parameter estimation of FH signals in alpha stable noise environment

Novel compound multistable stochastic resonance weak signal detection

Abstract The research on stochastic resonance (SR) which is used to extract weak signals from noisy backgrounds is of great theoretical significance and promising application. To address the shortcomings of the classical tristable SR model, this article proposes a novel compound multistable stochastic resonance (NCMSR) model by combining the Woods–Saxon (WS) and tristable models. The influence of the parameters of the NCMSR systems on the output response performance is studied under different α stable noises. Meanwhile, the adaptive synchronization optimization algorithm based on the proposed model is employed to achieve periodic and non-periodic signal identifications in α stable noise environments. The results show that the proposed system model outperforms the tristable system in terms of detection performance. Finally, the NCMSR model is applied to 2D image processing, which achieves great noise reduction and image recovery effects.

Research and application of composite stochastic resonance in enhancement detection

Aiming at the problem that the intermediate potential part of the traditional bistable stochastic resonance model cannot be adjusted independently, a new composite stochastic resonance (NCSR) model is proposed by combining the Woods–Saxon (WS) model and the improved piecewise bistable model. The model retains the characteristics of the independent parameters of WS model and the improved piecewise model has no output saturation, all the parameters in the new model have no coupling characteristics. Under α stable noise environment, the new model is used to detect periodic signal and aperiodic signal, the detection results indicate that the new model has higher noise utilization and better detection effect. Finally, the new model is applied to image denoising, the results showed that under the same conditions, the output peak signal-to-noise ratio (PSNR) and the correlation number of NCSR method is higher than that of other commonly used linear denoising methods and improved piecewise SR methods, the effectiveness of the new model is verified.

Robust Modulation Classification Over $\alpha$-Stable Noise Using Graph-Based Fractional Lower-Order Cyclic Spectrum Analysis

This paper introduces a novel automatic modulation classification (AMC) method using the graph-based fractional lower-order cyclic-spectrum analysis in the $\alpha$ -stable noise environment. The noise in the practical communication scenario usually exhibits impulse characteristics in the statistical sense, which could be modeled as the $\alpha$ -stable distribution. This would make the second- or higher-order statistics of the received signal vanish, and thus the performances of the conventional AMC algorithms designed for Gaussian noise significantly deteriorate when directly employed in the $\alpha$ -stable noise. In our proposed framework, the fractional lower-order cyclic spectrum (FLOCS) analysis is first invoked to acquire the polyspectra of the signal corrupted by the $\alpha$ -stable noise. Then, the graph-based AMC mechanism is systematically established upon the graph representation of the FLOCS to identify the modulation type according to the discrepancies between the graph features derived from the training and test data. The performance of our proposed new algorithm is theoretically analyzed, and the correct classification probability $P_{\text{cc}}$ over the modulation candidate set is formulated analytically. The remarkable scalability and efficiency of our proposed approach for the modulation candidate set variation are also theoretically investigated in detail. Monte Carlo simulation results demonstrate the effectiveness and superiority of the proposed AMC scheme.

Kernel Affine Projection P-norm (KAPP) Filtering under Alpha Stable Distribution Noise Environment

Aiming at improving the performance of the nonlinear adaptive filtering under the alpha-stable distribution noise environment, Kernel Affine Projection P-norm (KAPP) algorithm based on the minimum dispersion coefficient criterion and the affine projection is deduced. The accuracy of the gradient estimation is enhanced by using the input signals and the error signals at multiple times. The simulation results on Mackey–Glass chaotic time series prediction show that the KAPP algorithm has faster convergence speed, better steady-state performance and stronger robustness under the Gaussian noise and stable distributed noise environment.

A novel parameter-induced adaptive stochastic resonance system based on composite multi-stable potential model

Stochastic resonance (SR) is used widely as a weak signal detection method by using noise in many fields. In order to improve the weak signal processing capability of SR, a novel composite multi-stable model is proposed, which is constructed by the joint of the tristable model and the Gaussian Potential (GP) model. The SR system based on this model is constructed and the signal-to-noise ratio (SNR) is regarded as the index to measure the SR effect. The differential brain storm optimization (DBSO) algorithm is used to optimize the system parameters collaboratively to achieve parameter-induced adaptive SR. The influences of the system parameters V and R and the noise intensity D on the output response of SR system are analyzed under Gaussian white noise and α stable noise environments, and the advantages of the composite multi-stable SR system over the traditional tristable system are verified. For different levels of weak signals, the output performances of SR systems based on composite multi-stable model, traditional tristable model, composite tristable model are compared and analyzed. The results prove that the proposed model has better performance. Meanwhile, the adaptive detection of the multiple high-frequency weak signal is realized using the composite multi-stable SR system. The simulation results show that the proposed system has strong weak signal processing capability and good immunity to noise types, which widens the application range of SR in practical engineering.

A WHT Signal Detection-Based FLO-TF-UBSS Algorithm Under Impulsive Noise Environment

Linear frequency modulated (LFM) signal is used to describe radar echo signal, and Wigner–Hough transform (WHT) is a useful detection tool for LMF signals. The noise in radar echo signal has strong pulse under complex environment, which belongs to $$\alpha $$ stable distribution process. The WHT method degenerates under $$\alpha $$ stable distribution environment. Hence, fractional lower-order pseudo-Wigner–Ville distribution (FLO-PWVD) time–frequency method is introduced, and fractional lower-order pseudo-Wigner–Hough transform (FLO-PWHT) method based on the FLO-PWVD method and Hough transform is proposed for the detection of LFM signals. When the LFM signals are overlapped in time domain and generalized signal-to-noise ratio (GSNR) is relatively low, the FLO-PWHT method degenerate, even which cannot work. Therefore, fractional lower-order spacial time–frequency matrix is applied to substitute spacial time–frequency distribution matrix, and a new fractional lower-order spacial time–frequency underdetermined blind source separation (FLO-TF-UBSS) algorithm employing the time–frequency underdetermined blind source separation algorithm (TF-UBSS) is proposed in this paper. Also, we combine the FLO-PWHT method with the FLO-TF-UBSS algorithm and propose a fractional lower-order spatial time–frequency underdetermined blind source separation pseudo-Wigner–Hough transform (FLO-TF-UBSS-PWHT) method. The simulation results show that the FLO-PWHT method is obviously better than the existing PWHT algorithm under $$\alpha $$ stable distribution noise or Gaussian noise environment, which is robust. The FLO-TF-UBSS algorithm can effectively downgrade mean square error of the reconstructed LFM signals, and its performance is better than the existing TF-UBSS and minimum dispersion BSS algorithms. The FLO-TF-UBSS-PWHT algorithm can work well in the cases of time domain overlapping and relatively low GSNR; its performance is better than the FLO-PWHT algorithm, which has certain toughness. Finally, we apply the FLO-PWVD, FLO-TF-UBSS, and FLO-PWHT methods to analyze and extract fault features of the bearing outer race fault signal in DE; the experimental results illustrate their performance superiority.

Improved-Variable-Forgetting-Factor Recursive Algorithm Based on the Logarithmic Cost for Volterra System Identification

Compared with the least-mean-square algorithm, the least mean $p$ th power algorithm shows a better robustness performance against impulsive noises such as the $\alpha$ -stable noises. However, it still exhibits slow convergence rate and high kernel misadjustment. To overcome this drawback, a novel recursive logarithmic least mean $p$ th (RLLMP) algorithm is proposed for the Volterra system identification under $\alpha$ -stable noise environments. Instead of minimizing the $p$ th power, the new algorithm aims to minimize the $p$ th logarithmic cost, which makes it more robust against impulsive interferences. Furthermore, to enhance tracking performance, an improved variable forgetting factor (IVFF) algorithm (IVFF-RLLMP) is proposed, which is based on the robust estimation of outliers. Simulation results are presented to demonstrate the improved performance of the RLLMP and IVFF-RLLMP.

Parameter estimation of frequency hopping signals based on the Robust S-transform algorithms in alpha stable noise environment

In many communication systems the noise is non-Gaussian and usually exhibits impulsive characteristics. The alpha stable distribution, which is the only distribution satisfies the generalized central-limit theorem and characterizes a range of behaviors from Gaussian to extremely impulsive signals, is appropriate to model this type of noise. Since the performance of the S-transform (ST) based frequency hopping (FH) signal parameter estimation methods, which are very effective in Gaussian noise, may deteriorate significantly in the presence of impulsive noise, two robust methods based on the ST, i.e., the fractional lower order ST (FLOST) and the iterative ST (IST), are proposed in this paper. Specifically, the FLOST is the combination of fractional lower order statistics and the ST, whereas the IST is the application of the minimax Huber's robust theory to the ST. Both robust ST algorithms exhibit higher time-frequency concentration in the presence of alpha stable noise. Simulation results show that the proposed algorithms are valid for FH signal parameter estimation, and they distinctively outperform the standard ST in impulsive noise environment.

Adaptive Polynomial Filtering using Generalized Variable Step-Size Least Mean pth Power (LMP) Algorithm

This correspondence presents the adaptive polynomial filtering using the generalized variable step-size least mean $$p$$ p th power (GVSS-LMP) algorithm for the nonlinear Volterra system identification, under the $$\alpha $$ ? -stable impulsive noise environment. Due to the lack of finite second-order statistics of the impulse noise, we espouse the minimum error dispersion criterion as an appropriate metric for the estimation error, instead of the conventional minimum mean square error criterion. For the convergence of LMP algorithm, the adaptive weights are updated by adjusting $$p\ge 1$$ p ? 1 in the presence of impulsive noise characterized by $$1<\alpha <2$$ 1 < ? < 2 . In many practical applications, the autocorrelation matrix of input signal has the larger eigenvalue spread in the case of nonlinear Volterra filter than in the case of linear finite impulse response filter. In such cases, the time-varying step-size is an appropriate option to mitigate the adverse effects of eigenvalue spread on the convergence of LMP adaptive algorithm. In this paper, the GVSS updating criterion is proposed in combination with the LMP algorithm, to identify the slowly time-varying Volterra kernels, under the non-Gaussian $$\alpha $$ ? -stable impulsive noise scenario. The simulation results are presented to demonstrate that the proposed GVSS-LMP algorithm is more robust to the impulsive noise in comparison to the conventional techniques, when the input signal is correlated or uncorrelated Gaussian sequence, while keeping $$1<p<\alpha <2$$ 1 < p < ? < 2 . It also exhibits flexible design to tackle the slowly time-varying nonlinear system identification problem.

Stochastic Resonance in a Simple Threshold Sensor System with Alpha Stable Noise

We investigate the effect of alpha stable noise on stochastic resonance in a single-threshold sensor system by analytic deduction and stochastic simulation. It is shown that stochastic resonance occurs in the threshold system in alpha stable noise environment, but the resonant effect becomes weakened as the alpha stable index decreases or the skewness parameter of alpha stable distribution increases. In particular, for Cauchy noise a nonlinear relation among the optimal noise deviation parameter, the signal amplitude and the threshold is analytically obtained and illustrated by using the extreme value condition for the output signal-to-noise ratio. The results presented in this communication should have application in signal detection and image restoration in the non-Gaussian noisy environment.

Diffusion least‐mean P‐power algorithms for distributed estimation in alpha‐stable noise environments

We propose a diffusion least mean p-power (LMP) algorithm for distributed estimation in alpha stable noise environments, which is one of the widely used models that appears in various environments. Compared with the diffusion least mean squares (LMS) algorithm, better performance is obtained for the diffusion LMP methods when the noise is with alpha-stable distribution.

Stable Autoregressive Models and Signal Estimation

This article studies the problem of model identification and estimation for stable autoregressive process observed in a symmetric stable noise environment. A new tool called partial auto-covariation function is introduced to identify the stable autoregressive signals. The signal and noise parameters are estimated using a modified version of Generalized Yule Walker type method and the method of moments. The proposed methods are illustrated through data simulated from autoregressive signals with symmetric stable innovations. The new technique is applied to analyze the time series of sea surface temperature anomaly and compared with its Gaussian counterpart.

Indicators of Hearing Protection Use: Self-Report and Researcher Observation

Hearing protection devices (HPD) are commonly used to prevent occupational noise-induced hearing loss. There is a large body of research on hearing protection use in industry, and much of it relies on workers' self-reported use of hearing protection. Based on previous studies in fixed industry, worker self-report has been accepted as an adequate and reliable tool to measure this behavior among workers in many industrial sectors. However, recent research indicates self-reported hearing protection use may not accurately reflect subject behavior in industries with variable noise exposure. This study compares workers' self-reported use of hearing protection with their observed use in three workplaces with two types of noise environments: one construction site and one fixed industry facility with a variable noise environment, and one fixed industry facility with a steady noise environment. Subjects reported their use of hearing protection on self-administered surveys and activity cards, which were validated using researcher observations. The primary outcome of interest in the study was the difference between the self-reported use of hearing protection in high noise on the activity card and survey: (1) over one workday, and (2) over a 2-week period. The primary hypotheses for the study were that subjects in workplaces with variable noise environments would report their use of HPDs less accurately than subjects in the stable noise environment, and that reporting would be less accurate over 2 weeks than over 1 day. In addition to noise variability, other personal and workplace factors thought to affect the accuracy of self-reported hearing protection use were also analyzed. This study found good agreement between subjects' self-reported HPD use and researcher observations. Workers in the steady noise environment self-reported hearing protection use more accurately on the surveys than workers in variable noise environments. The findings demonstrate the potential importance of noise exposure variability as a factor influencing reporting accuracy.

Data block adaptive filtering algorithms for α-stable random processes

The least mean p-norm (LMP) algorithm is an effective algorithm for processing the signal of @a-stable distribution. This paper proposes data block adaptive filtering algorithms for the @a-stable random processes based on the fractional lower order statistics (FLOS). The data block algorithms change the direction of coefficient increment vector by introducing a matrix which includes the information of more past input signal vectors than which are used in the LMP algorithm during the iteration process, taking full advantage of the past values of the gradient vector during the adaptation. Simulations studies indicate that the proposed algorithms increase convergence rate in non-Gaussian stable distribution noise environments compared to the existing algorithms based on FLOS summarized in this paper.

A new blind source separation method based on fractional lower‐order statistics

AbstractWe proposed neural network structures related to multilayer feed‐forward networks for performing blind source separation (BSS) based on fractional lower‐order statistics. As alpha stable distribution process has no its second‐ or higher‐order statistics, we modified conventional BSS algorithms so that their capabilities are greatly improved under both Gaussian and lower‐order alpha stable distribution noise environments. We analysed the performances of the new algorithm, including the stability and convergence performance. The analysis is based on the assumption that the additive noise can be modelled as alpha stable process. The simulation experiments and analysis show that the proposed class of networks and algorithms is more robust than second‐order‐statistics‐based algorithm. Copyright © 2006 John Wiley &amp; Sons, Ltd.

Stochastic gradient algorithms for equalisation in [formula omitted]-stable noise

This paper addresses the problem of developing a least mean squares (LMS) style decision feedback equaliser algorithm for minimising bit error rate (BER) in impulsive noise environments characterised by the α -stable distribution. The development exploits the stable nature of the α -distribution and the concepts built on an earlier work in a Gaussian noise environment. Further, a Wiener-filter-with-limiter solution is also presented and used as a performance bench mark. An improvement in convergence and BER performance is achieved by using a minimum bit error rate (MBER) cost function instead of a conventional LMS-based design. The ability of least BER (LBER) equalisers based on a Gaussian noise assumption to operate in an α -stable noise environment is also highlighted.

Large scale cryogenic gravitational wave telescope

Large scale Cryogenic Gravitational wave Telescope (LCGT) is proposed to detect directly gravitational waves from neutron binary star coalescences at 240 Mpc away from our galaxy with signal-to-noise ration of 10. The designed strain sensitivity is 3 × 10 −24 1 / Hz at 100 Hz. To realize this sensitivity, LCGT is to be set in a stable and low seismic noise environment such as the Kamioka mine, to have sapphire cryogenic mirrors and suspension fibers, and to utilize broad band RSE technique combining with a power recycling technique. Two identical telescopes are set in the same site to enhance reliable detection. We will introduce the refined LCGT design.