Investigation of hearing aid users'speech understanding in noise and their spectral-temporal resolution skills

Purpose:Our study aims to compare speech understanding in noise and spectral-temporal resolution skills with regard to the degree of hearing loss,age,hearing aid use experience and gender of hearing aid users.Methods:Our study included sixty-eight hearing aid users aged between 40-70 years,with bilateral mild and moderate symmetrical sensorineural hearing loss.Random gap detection test,Turkish matrix test and spectral-temporally modulated ripple test were implemented on the participants with bilateral hearing aids.The test results acquired were compared statistically according to different variables and the correlations were examined.Results:No statistically significant differences were observed for speech-in-noise recognition,spectraltemporal resolution among older and younger adults in hearing aid users(p>0.05).There wasn’t found a statistically significant difference among test outcomes as regards different hearing loss degrees(p>0.05).Higher performances were obtained in terms of temporal resolution in male participants and participants with more hearing aid use experience(p<0.05).Significant correlations were obtained between the results of speech-in-noise recognition,temporal resolution and spectral resolution tests performed with hearing aids(p<0.05).Conclusion:Our study findings emphasized the importance of regular hearing aid use and it showed that some auditory skills can be improved with hearing aids.Observation of correlations among the speechin-noise recognition,temporal resolution and spectral resolution tests have revealed that these skills should be evaluated as a whole to maximize the patient’s communication abilities.

Spectral transformation-based technique for reducing effect of limited pre-correlation bandwidth in the GNSS receiver filter in presence of noise and multipath

This paper proposes an efficient scheme to reduce the pre-correlation bandwidth effect in the global navigation satellite system(GNSS)receiver filtering process.It is mainly based on the application of a spectral transformation to the satellite-emitted signal that effectively reduces its band.At the receiver's end,this operation causes the spreading of noise over a much wider band than that used by the radio frequency stage.Consequently,the resulting auto-correlation function in the acquisition process acquires properties that enhance considerably the performance of the receiver in the presence of the multipath and noise disturbing phenomena.The simulation results demonstrate that the proposed method is a plausible solution for both multipath and noise problems in the GNSS applications for any limited value of the pre-correlation bandwidth in the receiver filter.

Vibrational resonance in globally coupled bistable systems under the noise background

Effects of system size,coupling strength,and noise on vibrational resonance(VR)of globally coupled bistable systems are investigated.The power spectral amplifications obtained by the three methods all show that the VR exists over a wide range of parameter values.The increase in system size induces and enhances the VR,while the increase in noise intensity suppresses and eventually eliminates the VR.Both the stochastic resonance and the system size resonance can coexist with the VR in different parameter regions.This research has potential applications to the weak signal detection process in stochastic multi-body systems.

Noise Removal in Speech Processing Using Spectral Subtraction

Spectral subtraction is used in this research as a method to remove noise from noisy speech signals in the frequency domain. This method consists of computing the spectrum of the noisy speech using the Fast Fourier Transform (FFT) and subtracting the average magnitude of the noise spectrum from the noisy speech spectrum. We applied spectral subtraction to the speech signal “Real graph”. A digital audio recorder system embedded in a personal computer was used to sample the speech signal “Real graph” to which we digitally added vacuum cleaner noise. The noise removal algorithm was implemented using Matlab software by storing the noisy speech data into Hanning time-widowed half-overlapped data buffers, computing the corresponding spectrums using the FFT, removing the noise from the noisy speech, and reconstructing the speech back into the time domain using the inverse Fast Fourier Transform (IFFT). The performance of the algorithm was evaluated by calculating the Speech to Noise Ratio (SNR). Frame averaging was introduced as an optional technique that could improve the SNR. Seventeen different configurations with various lengths of the Hanning time windows, various degrees of data buffers overlapping, and various numbers of frames to be averaged were investigated in view of improving the SNR. Results showed that using one-fourth overlapped data buffers with 128 points Hanning windows and no frames averaging leads to the best performance in removing noise from the noisy speech.

A Noise-Resistant Superpixel Segmentation Algorithm for Hyperspectral Images

The superpixel segmentation has been widely applied in many computer vision and image process applications.In recent years,amount of superpixel segmentation algorithms have been proposed.However,most of the current algorithms are designed for natural images with little noise corrupted.In order to apply the superpixel algorithms to hyperspectral images which are always seriously polluted by noise,we propose a noiseresistant superpixel segmentation(NRSS)algorithm in this paper.In the proposed NRSS,the spectral signatures are first transformed into frequency domain to enhance the noise robustness;then the two widely spectral similarity measures-spectral angle mapper(SAM)and spectral information divergence(SID)are combined to enhance the discriminability of the spectral similarity;finally,the superpixels are generated with the proposed frequency-based spectral similarity.Both qualitative and quantitative experimental results demonstrate the effectiveness of the proposed superpixel segmentation algorithm when dealing with hyperspectral images with various noise levels.Moreover,the proposed NRSS is compared with the most widely used superpixel segmentation algorithm-simple linear iterative clustering(SLIC),where the comparison results prove the superiority of the proposed superpixel segmentation algorithm.

Near-surface structure from ambient-noise tomography and horizontal-to-vertical spectral ratio beneath the Nankou-Sunhe fault

Active faults pose a great threat to urban security.As the largest NW-trend active fault in Beijing area,the Nankou-Sunhe fault plays an important role in earthquake disaster and city construction.In this study,we collect continuous ambient noise data recorded by 43 temporary short-period seismograph between September 21th to October 12th 2019 to investigate the near-surface structure beneath the Nankou-Sunhe fault by using ambient noise tomography(ANT)and horizontal-to-vertical spectral ratio(HVSR)method.From ambient noise processing,fundamental-mode Rayleigh wave signals are clearly observed in the frequency band of 0.4-2.5 Hz.Then direct surface-wave inversion algorithm is applied to calculate the 3D shear-wave velocity model.Our results show that there is a sharp velocity contrast across the Nankou-Sunhe fault,with low velocities down to about 2 km on the hanging wall and high velocity on the footwall of the fault.According to the geological investigation,the low velocities are related to thicker sediments and Jurassic volcanic rock below which are the cap rock of the hydrothermal system.From the HVSR analysis,the HVSR curves of the sites near the fault shows double-peak,one less than 1 Hz and the other centered 7 Hz.After converting frequency to depth by the empirical equation,the results show that the thickness of sediments is thinned from southwest to northeast,which generally agrees well to field survey.Our results provide high-resolution near-surface structure for future study on disaster risk reduction and urban planning.

CROSS-SPECTRAL MOMENT AND SVD METHODS ESTIMATING THE PARAMETERS OF CLOSE SINUSOIDS IN COLORED NOISE

The cross-spectral estimation methods are efficient in estimating the parameters of sinusoidal signals embedded in colored noise. But up to now, only FPT and cross-periodogram methods are used in this field, the modern auto-spectral estimation method is introduced into cross-spectral estimation in this paper, meanwhile the cross-correlation based Yule-Walker equation is proposed theoretically and the moment and singular-value decomposition (SVD)) algorithms for cross-spectral estimation have been developed. Finally, a numerical example is given for comparing the presented methods with the well-known Cadzow’s SVD method.

Degradation of effective carrier-power-to-noise densityratio based on code trackingspectral sensitivitycoefficient for GNSS radio frequencycompatibilityin C band

The frequency band between 5 010 MHz and 5 030 MHz allocated as C band has been used as a candidate in the global navigation satellite systems （GNSS） along with more and more naviga- tion services in L band. The potential benefits and technical requirements of C band for satellite navi- gation have been analyzed before. However the degradation of effective carrier-power-to-noise densi- ty ratio（ A （C/No ）eu） based on code tracking spectral sensitivity coefficient（ CT_SSC ） as a compati- bility assessment methodology for potential GNSS radio frequency compatibility in C-Band has not been discussed clearly. So the compatibility of the signals in the C band between BeiDou （BD） B1 C and GPS L1C, L1C/A, Galileo E1Os as the interoperability or classical signals in L band is analyzed. Simulation results reveal the interference degree between BD III B1C and GPS L1C/A, L1C, Galileo E1OS. The results can also reveal that the multiplexed binary offset carrier （MBOC） and binary phase shift keying （BPSK） modulation is not appropriate for C band.

Well-Posedness and Finite Element Approximations for Elliptic SPDEs with Gaussian Noises

The paper studies the well-posedness and optimal error estimates of spectral finite element approximations for the boundary value problems of semi-linear elliptic SPDEs driven by white or colored Gaussian noises.The noise term is approximated through the spectral projection of the covariance operator,which is not required to be commutative with the Laplacian operator.Through the convergence analysis of SPDEs with the noise terms replaced by the projected noises,the well-posedness of the SPDE is established under certain covariance operator-dependent conditions.These SPDEs with projected noises are then numerically approximated with the finite element method.A general error estimate framework is established for the finite element approximations.Based on this framework,optimal error estimates of finite element approximations for elliptic SPDEs driven by power-law noises are obtained.It is shown that with the proposed approach,convergence order of white noise driven SPDEs is improved by half for one-dimensional problems,and by an infinitesimal factor for higher-dimensional problems.

Occurring Mechanism and Restraining Method Research of Numerical Noise Signal in Penetration Simulation

In hard target penetration simulation, the existing researches of the convergence of results are mainly concentrating in the corresponding relationship between penetration depth and mesh scales. However, the influence of the mesh difference on the penetration resistance and acceleration signals are seldom refer to. This paper presents the occurring mechanism and restraining method of numerical noise signal in penetration simulation. First, the concept of the noise signal izs proposed. By taking a 3D penetration simulation as example, the influence of the noise signal on the penetration resistance in different mesh scales and impact velocity is studied. To ensure the convergence of the computational results, the grid scale of the target is encrypted to 1：1：8. In addition, modem spectrum analysis method is introduced to further analyze the penetration resistance signal. The research results presented is useful to improve the computational accuracy of high speed projectile penetration simulation, and provide important reference for carrying out structural design and optimization of fuze system.

Extraction of Signals Buried in Noise: Non-Ergodic Processes

In this paper, we propose extraction of signals buried in non-ergodic processes. It is shown that the proposed method extracts signals defined in a non-ergodic framework without averaging or smoothing in the direct time or frequency domain. Extraction is achieved independently of the nature of noise, correlated or not with the signal, colored or white, Gaussian or not, and locations of its spectral extent. Performances of the pro-posed extraction method and comparative results with other methods are demonstrated via experimental Doppler velocimetry measurements.

Extraction of Buried Signals in Noise: Correlated Processes

In this paper, we propose extraction of signals correlated with noise in which they are buried. Proposed extraction method uses no a-priori information on the buried signal and works independently of the nature of noise, correlated or not with the signal, colored or white, Gaussian or not, and locations of its spectral extent. Extraction of buried correlated signals is achieved without averaging in the time or frequency domain.

Under-ice ambient noise in the Arctic Ocean: observations at the long-term ice station

Under-ice ambient noise in the Arctic Ocean is studied using the data recorded by autonomous hydrophones at the long-term ice station during the 9th Chinese National Arctic Research Expedition.Time-frequency analysis of two 7-s-long ice-induced noise samples shows that both ice collision and ice breaking noise have many outliers in the time-domain(impulsive characteristic)and abundant frequency components in the frequency-domain.Ice collision noise lasts for several seconds while the duration of ice breaking noise is much shorter(i.e.,less than tens of milliseconds).Gaussian distribution and symmetric alpha stable(sαs)distribution are used in this paper to fit the impulsive under-ice noise.The sαs distribution can achieve better performance as it can track the heavy tails of impulsive noise while Gaussian distribution fails.This paper also analyzes the meteorological variables during the under-ice noise observation experiment and deduces that the impulsive ambient noise was caused by the combined force of high wind speed and increasing atmosphere temperature on the ice canopy.The Pearson correlation coefficients between long-term power spectral density variations of under-ice ambient noise and meteorological variables are also studied in this paper.

Dual transponder ranging performance in the presence of oscillator phase noise

A dual transponder carrier ranging method can be used to measure inter-satellite distance with high precision by combining the reference and the to-and-fro measurements. Based on the differential techniques, the oscillator phase noise, which is the main error source for microwave ranging systems, can be significantly attenuated. Further, since the range measurements are derived on the same satellite, the dual transponder ranging system does not need a time tagging system to synchronize the two satellites. In view of the lack of oscillator noise analysis on the dual transponder ranging model, a comprehensive analysis of oscillator noise effects on ranging accuracy is provided. First, the dual transponder ranging system is described with emphasis on the detailed analysis of oscillator noise on measurement precision. Then, a high-fidelity numerical simulation approach based on the power spectrum density of an actual ultra-stable oscillator is carried out in both frequency domain and time domain to support the presented theoretical analysis. The simulation results under different conditions are consistent with the proposed concepts, which makes the results reliable. Besides, the results demonstrate that a high level of accuracy can be achieved by using this oscillator noise cancelation-oriented ranging method.

Research on Real-time Monitoring of Abnormal Seismic Noise

The noise data in vertical component records of 85 seismic stations in Fujian Province during 2012 is used as the research object in this paper. The noise data is divided into fiveminute segments to calculate the power spectra. The high reference line and low reference line of station are then identified by drawing a probability density function graph( PDF)using the power spectral probability density function. Moreover, according to the anomalies of PDF graphs in 85 seismic stations,the abnormal noise is divided into four categories: dropped packet, low noise, high noise, and median noise anomalies.Afterwards,four selection methods are found by the high or low noise reference line of the stations,and the system of real-time monitoring of seismic noise is formed by combining the four selection methods. Noise records of 85 seismic stations in Fujian Province in July2013 are selected for verification,and the results show that the anomalous noise-recognition system could reach a 90% success rate at most stations and the effect of selection are very good. Therefore,it could be applied to the seismic noise real-time monitoring in stations.

SPEECH ENHANCEMENT BASED ON DYNAMIC NOISE ESTIMATION WITHIN AUTO-CORRELATION DOMAIN

Most noise suppression algorithms of single channel use the mean of noisy segments to estimate the characteristics of noise spectrum, ignoring the estimation of noise in speech segments. Therefore, when the energy level of noise varies with the time, the performance of removing noise will be degraded. To solve this problem, a speech enhancement approach based on dynamic noise estimation within correlation domain was proposed. This method exploits the characteristics that noise energy mainly concentrates on 0 th order correlation coefficients, signal is auto correlated but signal and noise, noise and noise are uncorrelated, then estimates and decomposes the noise, thus helps to solve the above mentioned problem. The results of recognition experiments on speech signals of 15 Chinese cities’ names corrupted by noise of exhibition hall shows, this approach is better than SS (Spectral Subtraction) method, adapts better to the variances of energy levels of speech signal corrupted by noise, has some practicability to improve the robustness of recognition systems under noisy environment.

Analysis on the noises from continuously monitoring GPS sites

In this paper, we analyze the time series of site coordinates of 27 continuously monitoring GPS sites covered by the Crustal Movement Observation Network of China over the whole country. The data are obtained in the period from the beginning of the observation to the November of 2005. On the basis of data processing, we analyze the power spectrum density of coordinate component noise at each site and calculate the spectral indexes manifesting the noise property of each component. The spectral indexes indicate that for most sites, the noise of time series of each coordinate component can be addressed by the model of white noise ＋ flicker noise; and for a small amount of sites, it can be described by the model of white noise ＋ flicker noise ＋ random walk noise. We also quantitatively estimate each noise component in the model by using the criterion of maximum likelihood estimation. The result shows that the white noise in the time series of GPS site coordinates does not constitute the main part of noise. Therefore, the error estimation of site movement parameters is usually too small, or too optimistic if we consider the white noise only. Correspondingly, if this factor is not fully considered in explaining these movement parameters, it might mislead the readers.

The background noise level of vertical pendulum tiltmeter in China

By using the observation data of the vertical pendulum tiltmeter in China,the influence of different sampling rates on the results of power spectral density is studied,and the background noise level of the seismic frequency band(200-600 s)of different stations and different types of instruments is emphatically analyzed.The conclusions are as follows:1.The results of both single station and national statistics show that the noise levels of the two types of pendulum tiltmeter equal in the seismic frequency band;2.There is no obvious regularity in the regional distribution,due to the influence of ocean and active tectonic movement,the noise levels of some stations in coastal areas are relatively high;3,The curves of high noise level instrument are generally not smooth and burr-prone.Except for some stations affected by the ocean,they are mostly caused by the observation environment or the instrument itself.

New Wavelet Thresholding Algorithm in Dropping Ambient Noise from Underwater Acoustic Signals

Underwater Wireless Communication, largely dependent on the acoustic communication between the machines, is largely affected by various types of noise in the shallow and deep water. However ambient noise which is due to multiple sources (e.g. shipping, wind) and no one source dominates. Ambient noise masks the acoustic signal to a large extent. Hence today it has drawn the attention of the experts to reduce its effect on the received signal. This paper discusses ambient noise problem and devises a new wavelet thresholding method to reduce its effect. Afterwards a comparative study on statistical parameters is shown to prove the efficiency of the devised method.

改进粒子滤波跟踪的视听双模态语音识别仿真

噪声环境下视听语音不易被识别,为提升语音识别效果,提出改进粒子滤波跟踪的视听双模态语音识别方法。采用谱减法去除噪声数据,完成视听双模态语音的消噪处理;根据人语和唇动信息之间的相关性,采用改进粒子滤波跟踪方法提取视听双模态语音特征信息,构建transformer语音识别模型,将提取的特征信息输入到模型内实施并行训练,实现视听双模态语音的有效识别。实验结果表明,通过对上述方法开展信噪比测试、识别性能测试,验证了上述方法的可行性高、可靠性强。