A Correlation Polarimeter for Noise-Like Signals

P174 ATTENUATING MULTIPLES FOR LOW SIGNAL- TO-NOISE RATIO DATA USING OPTIMAL BEAMFORMING 1 Abstract This paper presents the theory of optimal beamforming and its application in attenuating multiples for low signalto-noise (S/N) ratio data. The key point of the theory is updating the constraint in optimal beamformer design by using adaptive beamformer with the quiescent weighted vector. Real data example shows that the newly developed optimal beamformer has improved the minimum variance unbiased (MVU) beamformer significantly in attenuating multiples for low folding and/or low S/N ratio data without much extra cost. Introduction The objective of seismic data processing is to

The generalized mutual information (GMI) of bit-interleaved coded modulation (BICM) systems, sometimes called the BICM capacity, is investigated at low signal-to-noise ratio (SNR). The combinations of input alphabet, input distribution, and binary labeling that achieve the Shannon limit <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${- 1.59}\;{\rm dB}$</tex></formula> are completely characterized. The main conclusion is that a BICM system with probabilistic shaping achieves the Shannon limit at low SNR if and only if it can be represented as a zero-mean linear projection of a hypercube. Hence, probabilistic shaping offers no extra degrees of freedom to optimize the low-SNR BICM-GMI, in addition to what is provided by geometrical shaping. The analytical conclusions are confirmed by numerical results, which also show that for a fixed input alphabet, probabilistic shaping can improve the BICM-GMI in the low and medium SNR range.

The impact of the distance spectrum and interleaver structure on the bit error probability of turbo codes is considered. A new turbo code design method for Gaussian channels is presented. The proposed method combines a search for good component codes with interleaver design. The optimal distance spectrum is used as the design criterion to construct good turbo component codes at low signal-to-noise ratios (SNRs). In addition, an interleaver design method is proposed. This design improves the code performance at high SNR. Search for good component codes at low SNR is combined with a code matched interleaver design. This results in new turbo codes with a superior error performance relative to the best known codes at both low and high SNR. The performance is verified by both analysis and simulation.

A precise estimation of SFO (Sampling Frequency Offset) is an important issue in OFDM (Orthogonal Frequency Division Multiplexing) system. However, the conventional method causes serious performance degradation of SFO estimation in low SNR (Signal to Noise Ratio) or large Doppler frequency environment. Therefore, in this paper, we propose a SFO estimation method which can achieve stable operation in low SNR and large Doppler frequency environment. The proposals for SFO estimation /compensation are mainly based on DVB (Digital Video Broadcasting) system, and we verified that the proposed method has a good performance and stable operation through extensive simulation.

A precise estimation and compensation of SFO (Sampling Frequency Offset) is an important issue in OFDM (Orthogonal Frequency Division Multiplexing) system, because sampling frequency mismatch between the transmitter and the receiver dramatically degrades the system performance due to the loss of orthogonality between the subcarriers. However, the conventional method causes serious performance degradation of SFO estimation in low SNR (Signal to Noise Ratio) or large Doppler frequency environment. Therefore, in this paper, we propose two SFO estimation methods which can achieve stable operation in low SNR and large Doppler frequency environment. The proposals for SFO estimation / compensation are mainly specialized on DVB (Digital Video Broadcasting) system, and we verified that the proposed method has good performance and stable operation through extensive simulation.

In the last few years, the demand of robust beamforming techniques has been increased significantly to serve numerous purposes. However, beamforming under low Signal to Noise Ratio (SNR) condition has always been a challenging task, which becomes more tedious in case of multiple sources. On the other hand, there are numerous factors such as steering vector error or similar estimation error problems that confine the efficiency of major beamforming techniques. Classical methods employ Eigen-Value Decomposition (EVD) and Singular Value Decomposition (SVD) over covariance matrix of the received signal; however their efficiency is limited under low SNR and multi-source condition, and perform inferiorly in case of steering vector errors. Such approaches are limited to achieve optimal Direction-of-Arrival (DOA) estimation that degrades the overall performance of the beamformer. Considering all these limitations, in this research, the focus is made on designing a robust DOA estimation model, followed by a novel beamformer for 8-element Uniform Linear Array (ULA). The simulation work consists two parts DOA estimation and beamforming. Unlike classical MUSIC and ESPRIT algorithms popular for DOA estimation our proposed model exploits the propagation method based subspace method where at first covariance matrix is obtained for the received signal, which is followed by normalization and Cross-Correlation matrix estimation. The obtained correlation matrix is processed for Generalized Eigen-Value Decomposition (GEVD) that efficiently obtained (orthogonal) subspace and SOI separation to yield DOA information. This approach enables more efficient DOA estimation even under low-SNR and steering vector error conditions. After obtaining DOA information, a novel adaptive beamformer is designed by using MVDR and FROST in parallel. DOA information from proposed model is given as input to the adaptive MVDR FROST beamforming model. Exploiting dynamic Signal-to-Interference ratio (SINR) by each beamformer our proposed method introduces an adaptation model that intends to achieve higher SINR value. MATLAB 2018a based simulation confirmed the robustness of the proposed model in terms of DOA estimation and beamforming results.

Intelligent spectral algorithm for pigments visualization, classification and identification based on Raman spectra

In this paper, a robust multiple observation likelihood ratio test (MOLRT) based voice activity detection (VAD) method is proposed. At the beginning of this paper, we introduce the Wiener filter to the observed signal in time domain which can help mitigating some noise. The reason why we use the Wiener filter for VAD is that the performance of the VAD is always better in high signal to noise ratio (SNR) range than in low SNR range. Then, some ideas are proposed to improve the performance of the MOLRT based VAD method. As we all know, a conventional MOLRT based method including three modules named likelihood ratio (LR) estimation, threshold setting and hangover technique. To improve the estimation accuracy of LR, we adopt the unbiased minimum mean-square error (MMSE) algorithm for noise power spectrum estimation in every frame, which is very effective for LR estimation in MOLRT-based VAD method. That is because the LR is a function of a prior and a posterior SNR and unbiased MMSE algorithm is very useful for noise estimation. In addition, to make our VAD method more robust, a dynamic threshold setting technique is proposed in our method, which is related to the minimum noise power spectrum. That is because minimum noise power spectrum can help us updating the value of threshold to a suitable level according to the denoised signal. Last but most important, a novel hangover algorithm is introduced in this paper comparing to the conventional HMM based hangover algorithm. In the novel hangover algorithm, the current frame is determined by the statistical result of the following speech/non-speech detections based on the likelihood ratio test. And the evaluation results reveal that proposed method significantly outperform the baseline result of LRT as regards VAD accuracy in both noise variations and low SNR conditions.

Background/Objectives: Speech understanding in noise relies on both temporal fine structure (TFS) and temporal envelope (ENV) cues. While TFS primarily conveys interaural time differences (ITDs) at low frequencies, ENV cues can also support ITD processing, especially when TFS is unavailable or degraded. Expanding the ENV by increasing modulation depth has been proposed to improve speech perception, but its effects on spatial release from masking (SRM) and binaural temporal processing in normal-hearing listeners remain unclear. The goal of this study was to evaluate the effect of ENV enhancement on SRM in young adults with normal hearing and its influence on ITD sensitivity and interaural coherence (IC). Method: Thirty normal-hearing native Kannada speakers (19-34 years) participated. Speech stimuli consisted of Kannada sentences embedded in four-talker babble at -5, 0, and +5 dB signal to noise ratio (SNR). Target and masker were spatialized using head-related transfer functions at 0°, 15°, and 37.5° azimuths. Stimuli were presented with and without ENV enhancement (compression-expansion algorithm). Speech recognition scores were analyzed using generalized linear mixed models, and SRM was calculated as performance differences between co-located and spatially separated conditions. Cross-correlation analyses were performed to estimate ITDs and IC across SNRs. Result: ENV enhancement yielded significantly higher SRM values across all SNRs and spatial separations. Benefits were greatest at lower SNRs and wider target-masker separations. Cross-correlation analysis showed enhanced IC and more reliable ITD estimates under the expanded condition, particularly at moderate SNRs. Conclusions: Temporal ENV enhancement strengthens spatial unmasking and binaural timing cues in normal-hearing adults, especially under adverse listening conditions. These findings highlight its potential application in auditory rehabilitation and hearing technologies where ENV cues are critical.

The research difficulties of speech detection in the present focus on the cases that the signal-to-noise ratio(SNR) is low and the background noise changes dramatically. For the problem of speech detection under low SNR environments, based on the sparsity of speech in frequency domain and the sparse representation ability in frequency domain of the over-complete Fourier basis, the speech signal is reconstructed with Matching Pursuit algorithm, and we propose a low SNR speech detection method which uses the short time energy of the reconstructed signal as a detection feature. The experimental results show that this algorithm exhibits higher robustness in the low SNR white noise environments.

This paper proposes a direction of arrival (DOA) estimation method that can work at low signal-to-noise ratios (SNR). And the method is based on the classical model Pix2Pix in generative adversarial networks. Using the Pix2Pix graph-to-graph conversion capability, the covariance matrix with low SNR is converted to a covariance matrix with high SNR, enabling high-precision DOA estimation at low SNR. Experiments show that the root-mean-square error (RMSE) of multiple signal classification (MUSIC) is more than 30 times higher than that of the method at a SNR of -10dB.

Maximum-likelihood (ML) estimation is an established paradigm for the assessment of imaging system performance in nonlinear quantitation tasks. At high signal-to-noise ratio (SNR), ML estimates are asymptotically Gaussian-distributed, unbiased and efficient, thereby attaining the Cramer–Rao bound (CRB). Therefore, at high SNR the CRB is useful as a predictor of the variance of ML estimates and, consequently, as a basis for measures of estimation performance. At low SNR, however, the achievable parameter variances are often substantially larger than the CRB and the estimates are no longer Gaussian-distributed. These departures imply that inference about the estimates that is based on the CRB and the assumption of a normal distribution will not be valid. We have found previously that for some tasks these effects arise at noise levels considered clinically acceptable. We have derived the mathematical relationship between a new measure, χ2pdf-ML, and the expected probability density of the ML estimates, and have justified the use of χ2pdf-ML-isocontours in parameter space to describe the ML estimates. We validated this approach by simulation experiments using spherical objects imaged with a Gaussian point spread function. The parameters, activity concentration and size, were estimated simultaneously by ML, and variances and covariances calculated over 1000 replications per condition from 3D image volumes and from 2D tomographic projections of the same object. At low SNR, where the CRB is no longer achievable, χ2pdf-ML-isocontours provide a robust prediction of the distribution of the ML estimates. At high SNR, the χ2pdf-ML-isocontours asymptotically approach the analogous χ2pdf-F-contours derived from the Fisher information matrix. The χ2pdf-ML model appears to be suitable for characterization of the influence of the noise level and characteristics, the task, and the object on the shape of the probability density of the ML estimates at low SNR. Furthermore, it provides unique insights into the causes of the variability of estimation performance.

Impact of SNR, masker type and noise reduction processing on sentence recognition performance and listening effort as indicated by the pupil dilation response

PurposeConfidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.Design/methodology/approachThe construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.FindingsThe GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.Originality/valueThis research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

Wireless channels with multiple transmit/receive antennas are known to provide a high spectral efficiency both when the channel is known to the receiver, and when the channel is not known to the receiver if the signal-to-noise ratio (SNR) is high. Here we analyze such systems at low SNR, which may find application in sensor networks and other low-power devices. The key point is that, since channel estimates are not reliable, it is often not reasonable to assume that the channel is known at the receiver at low SNR. In this unknown channel case, we show that for sensible input distributions, in particular all practical modulation schemes, the capacity is asymptotically quadratic in the SNR, /spl rho/, and thus much less than the known channel case where it exhibits a linear growth in /spl rho/. We show that under various signaling constraints, e.g., Gaussian modulation, unitary space-time modulation, and peak constraints, that mutual information is maximized by using a single transmit antenna. We also show that at low SNR, sending training symbols leads to a rate reduction in proportion to the fraction of training duration time so that it is best not to perform training. Furthermore, we show that the per-channel use mutual information is linear in both the number of receive antennas and the channel coherence interval.