Additive Noise Environment Research Articles

Adaptive algorithms for blind channel equalization in impulsive noise

The unsupervised adaptive mitigation of intersymbol interference in an additive impulsive noise environment, modeled as generalized Gaussian, is dealt in this work. The theory of statistical invariance, Wijsman’s theorem, is used to develop a maximal-invariant test to discriminate equally-likely pulsed signals against impulsive disturbance leading to an admissible cost function for blind equalization. The cost function is optimized to realize two adaptive equalizers capable of not only mitigating intersymbol interference but also robust to impulsive disturbance. Numerical simulations, obtained on a baseband digital microwave radio system for amplitude-phase shift keying signaling in an additive (generalized Gaussian and symmetric-alpha stable) impulsive environment, confirm the admissibility of the proposed equalizers in terms of robustness and steady convergence.

Leader selection for coherence in symmetric and asymmetric trees

This paper studies consensus dynamics in symmetric and asymmetric trees with leader selection under additive noise environment, which is characterized as leader–follower coherence determined by the spectrum. In order to compare the effect of the leader’s positions on the coherence, we choose a tree-like network model and employ three different ways to assign the leaders. Based on the tree construction, we obtain exact solutions for the leader–follower coherence depending on the number of leaders and network parameters. We then show that the consensus in symmetric and asymmetric trees becomes better with an increasing number of leaders. When the number of leaders and network parameters satisfy certain conditions, the leader’s positions and topological structures have a profound impact on the coherence.

Signal Detection in Generalized Gaussian Distribution Noise With Nakagami Fading Channel

A fractional order moments-based detector is proposed for the detection of weak signals in additive impulsive noise environment assumed as generalized Gaussian distribution with properly selected parameter values. The asymptotic detection performance is derived and compared with some traditional detectors optimized for operations in Gaussian noise with Nakagami fading communication channels. The analytical and computer simulation results of the fractional order moment-based detector are shown for signal detection with fading channels in the impulsive noise.

The analysis of additive noise of thermal test low-frequency cable fault detection based on M sequence correlation algorithm

The problem of low accuracy of off-line detecting on thermal test low-frequency cable faults could be solved by designing a cable fault detection system, based on FPGA program export M sequence code (linear feedback shift register sequence) as pulsed signal source. This paper discussed the design principle of spread spectrum time-domain reflectometry (SSTDR) reflection method, and introduced the hardware structure. Experimental platform established by this detection system could adapt to measurement of thermal test low-frequency cable with different lengths and could meet different additive noise environment. The off-line detecting on thermal test low-frequency cable faults could be solved by passing pre-filtering circuit and wave-shaping circuit processing, calculating transmitting and receiving pulse in transmit-receive buffer circuit, cable fault points could be located accurately through FPGA digital correlation algorithm. Testing data has shown that, in the M-sequence based SSTDR detection system, the additive noise and detected confidence can be described as a linear relationship.

Direct positioning maximum likelihood estimator using TDOA and FDOA for coherent short‐pulse radar

The intra-pulse phase change caused by frequency difference of arrival (FDOA) is very little and even omitted due to extremely short duration of emitter pulses that the joint location using time difference of arrival (TDOA) and FDOA cannot be realised. To solve this problem, a multi-pulse coherent accumulation algorithm of direct position determination (DPD) using the TDOA and FDOA is proposed in this study. Through establishing coherent signal model of short pulses which is deterministic but unknown, the maximum likelihood estimator for localisation of a stationary emitter using multiple moving receivers is derived by taking the DPD approach under additive Gaussian white noise environment. This study also derives a signal-specific Cramer-Rao lower bound (CRLB) of DPD, modelling the signal as a deterministic unknown. The CRLB is more applicable to some specific (non-stationary, non-Gaussian) signals, especially electromagnetic signals. Simulation results show that the proposed method achieves a significant performance improvement in locating accuracy over the existing methods and its accuracy is close to the CRLB in normal signal-to-noise ratio.

Robust Features for Speech Recognition using Temporal Filtering Technique in the Presence of Impulsive Noise

In this paper we introduce a robust feature extractor, dubbed as Modified Function Cepstral Coefficients (MODFCC), based on gammachirp filterbank, Relative Spectral (RASTA) and Autoregressive Moving-Average (ARMA) filter. The goal of this work is to improve the robustness of speech recognition systems in additive noise and real-time reverberant environments. In speech recognition systems Mel-Frequency Cepstral Coefficients (MFCC), RASTA and ARMA Frequency Cepstral Coefficients (RASTA- MFCC and ARMA-MFCC) are the three main techniques used. It will be shown in this paper that it presents some modifications to the original MFCC method. In our work the effectiveness of proposed changes to MFCC were tested and compared against the original RASTA-MFCC and ARMA-MFCC features. The prosodic features such as jitter and shimmer are added to baseline spectral features. The above-mentioned techniques were tested with impulsive signals under various noisy conditions within AURORA databases. Index Terms—Auditory filter, impulsive noise, MFCC, RASTA filter, ARMA filter, HMM\GMM.

Partial Reconstruction Based Compressed Spectrum Sensing

The compression perception applied in spectrum detection technology can break through the traditional Nyquist sampling theorem, reduce sampling rate, so it can reduce the pressure of hardware processing. Therefore suitable for use in spectrum detection technology, especially the broadband signal spectrum detection. This paper based on the compression perception principle, the paper proposes a way of not to reconstruct the broadband spectrum itself, but through the part of the reconstruction of cognitive radio spectrum detection technology. The simulation results show that in the additive gaussian white noise environment, through the stagewise orthogonal matching pursuit reconstruction detection technology and the traditional signal energy detection technology comparison, learned that this technology can greatly decrease the system complexity and improve performance of perception.

2-D DOA Joint Estimation Method Based on Polarization Array

A new joint method is presented to estimate the 2-D DOA and polarization parameters of far-field sources based on four order cumulant. The method adopts L array, and every sensor consists of orthogonal dipole-pairs with the same coordinate axis direction, which measure the x-axis and y-axis electric-field components of the far-field signals. The method constructs four four-order cumulant matrixes by receiving polarized array data and does not require any spectral peak search and parameter pairing, can be applied to additive Gaussian white noise or color noise environment. The performance of the proposed method is verified by computer simulations.

Robust feature extraction based on an asymmetric level-dependent auditory filterbank and a subband spectrum enhancement technique

In this paper we introduce a robust feature extractor, dubbed as robust compressive gammachirp filterbank cepstral coefficients (RCGCC), based on an asymmetric and level-dependent compressive gammachirp filterbank and a sigmoid shape weighting rule for the enhancement of speech spectra in the auditory domain. The goal of this work is to improve the robustness of speech recognition systems in additive noise and real-time reverberant environments. As a post processing scheme we employ a short-time feature normalization technique called short-time cepstral mean and scale normalization (STCMSN), which, by adjusting the scale and mean of cepstral features, reduces the difference of cepstra between the training and test environments. For performance evaluation, in the context of speech recognition, of the proposed feature extractor we use the standard noisy AURORA-2 connected digit corpus, the meeting recorder digits (MRDs) subset of the AURORA-5 corpus, and the AURORA-4 LVCSR corpus, which represent additive noise, reverberant acoustic conditions and additive noise as well as different microphone channel conditions, respectively. The ETSI advanced front-end (ETSI-AFE), the recently proposed power normalized cepstral coefficients (PNCC), conventional MFCC and PLP features are used for comparison purposes. Experimental speech recognition results demonstrate that the proposed method is robust against both additive and reverberant environments. The proposed method provides comparable results to that of the ETSI-AFE and PNCC on the AURORA-2 as well as AURORA-4 corpora and provides considerable improvements with respect to the other feature extractors on the AURORA-5 corpus.

A Two Microphone-Based Approach for Source Localization of Multiple Speech Sources

This paper proposes a two microphone-based source localization technique for multiple speech sources utilizing speech specific properties and novel clustering algorithms. Voiced speech is sparse in the frequency domain and can be represented by sinusoidal tracks via sinusoidal modeling which provides high local signal-to-noise ratio (SNR). By utilizing the inter-channel phase differences (IPDs) between the dual channels on the sinusoidal tracks, the source localization of the mixed multiple speech sources is turned into a clustering problem on the IPD versus frequency plot. The generalized mixture decomposition algorithm (GMDA) is used to cluster the groups of points corresponding to multiple sources and thus estimate the direction of arrival (DOA) of the sources. Experiments illustrate the proposed GMDA algorithm with the Laplacian noise model can estimate the number of sources accurately and exhibits smaller DOA estimation error than the baseline histogram based DOA estimation algorithm in various scenarios including reverberant and additive white noise environments. Experiments suggest that appropriate power thresholding can be a simple and good approximation to the sinusoidal modeling, for the purpose of selecting time-frequency points with high local SNR, with slight loss in performance.

On the Performance Analysis of a Class of Transform-domain NLMS Algorithms with Gaussian Inputs and Mixture Gaussian Additive Noise Environment

This paper studies the convergence performance of the transform domain normalized least mean square (TDNLMS) algorithm with general nonlinearity and the transform domain normalized least mean M-estimate (TDNLMM) algorithm in Gaussian inputs and additive Gaussian and impulsive noise environment. The TDNLMM algorithm, which is derived from robust M-estimation, has the advantage of improved performance over the conventional TDNLMS algorithm in combating impulsive noises. Using Price's theorem and its extension, the above algorithms can be treated in a single framework respectively for Gaussian and impulsive noise environments. Further, by introducing new special integral functions, related expectations can be evaluated so as to obtain decoupled difference equations which describe the mean and mean square behaviors of the TDNLMS and TDNLMM algorithms. These analytical results reveal the advantages of the TDNLMM algorithm in impulsive noise environment, and are in good agreement with computer simulation results.

Incorporating Codebook and Utterance Information in Cepstral Statistics Normalization Techniques for Robust Speech Recognition in Additive Noise Environments

Cepstral statistics normalization techniques have been shown to be very successful at improving the noise robustness of speech features. This letter proposes a hybrid-based scheme to achieve a more accurate estimate of the statistical information of features in these techniques. By properly integrating codebook and utterance knowledge, the resulting hybrid-based approach significantly outperforms conventional utterance-based, segment-based and codebook-based approaches in additive noise environments. Furthermore, the high-performance CS-HEQ can be implemented with a short delay and can thus be applied in real-time online systems.

Comments on "A Recursive Least M-Estimate Algorithm for Robust Adaptive Filtering in Impulsive Noise: Fast Algorithm and Convergence Performance Analysis

recent paper [S. C. Chan and Y. X. Zou, A Recursive Least M-Estimate Algorithm for Robust Adaptive Filtering in Impulsive Noise: Fast Algorithm and Convergence Performance Analysis, IEEE Transactions on Signal Processing, vol. 57, no. 1, Jaunary 2008] studied the behavior of a recursive least M-estimate (RLM) adaptive filtering algorithm in an additive impulsive noise environment. The mean and mean-square behavior of the algorithm was analyzed using a joint Gaussian assumption for the input and the error signal. This note points out that this assumption contradicts the probability model for the impulsive noise [contaminated Gaussian (CG) noise]. Hence, the analytic results presented in Chan and Zou are of limited interest.

Perceptual features for automatic speech recognition in noisy environments

The performances of two perceptual properties of the peripheral auditory system, synaptic adaptation and two-tone suppression, are compared for automatic speech recognition (ASR) in an additive noise environment. A simple method of synaptic adaptation as determined by psychoacoustic observations was implemented with temporal processing of speech utilizing a zero-crossing auditory model as a pre-processing front end. The concept is similar to RASTA processing, but instead of bandpass filters, a high-pass infinite impulse response (IIR) filter is used. It is shown that rapid synaptic adaptation may be implemented by temporal processing using the zero-crossing algorithm, not otherwise implementable in the spectral domain implementation. The two-tone suppression was implemented in the zero-crossing auditory model using a companding strategy. Recognition performances with the two perceptual features were evaluated on isolated digits (TIDIGITS) corpus using continuous density HMM recognizer in white, factory, babble and Volvo noise. It is observed that synaptic adaptation performs better in stationary white Gaussian noise. In presence of non-stationary non-Gaussian noise, however, no improvements or a degradation is observed. Moreover, a reciprocal effect is observed with two-tone suppression, with better performance in non-Gaussian real-world noise and degradation in stationary white Gaussian noise.

Cepstral Statistics Compensation and Normalization Using Online Pseudo Stereo Codebooks for Robust Speech Recognition in Additive Noise Environments

This paper proposes several cepstral statistics compensation and normalization algorithms which alleviate the effect of additive noise on cepstral features for speech recognition. The algorithms are simple yet efficient noise reduction techniques that use online-constructed pseudo-stereo codebooks to evaluate the statistics in both clean and noisy environments. The process yields transformations for both clean speech cepstra and noise-corrupted speech cepstra, or for noise-corrupted speech cepstra only, so that the statistics of the transformed speech cepstra are similar for both environments. Experimental results show that these codebook-based algorithms can provide significant performance gains compared to results obtained by using conventional utterance-based normalization approaches. The proposed codebook-based cesptral mean and variance normalization (C-CMVN), linear least squares (LLS) and quadratic least squares (QLS) outperform utterance-based CMVN (U-CMVN) by 26.03%, 22.72% and 27.48%, respectively, in relative word error rate reduction for experiments conducted on Test Set A of the Aurora-2 digit database.

Noisy Speech Feature Estimation on the Aurora2 Database using a Switching Linear Dynamic Model

This paper presents an approach to enhance speech feature estimation in the log spectral domain under additive noise environments. A switching linear dynamic model (SLDM) is explored as a parametric model for the clean speech distribution, enforcing a state transition in the feature space and capturing the smooth time evolution of speech conditioned on the state sequence. Experimental results using the Aurora2 database show that the new SLDM approach can improve speech enhancement performance in terms of recognition accuracy.

Multidimensional Multichannel FIR Deconvolution Using GrÖbner Bases

We present a new method for general multidimensional multichannel deconvolution with finite impulse response (FIR) convolution and deconvolution filters using Gröbner bases. Previous work formulates the problem of multichannel FIR deconvolution as the construction of a left inverse of the convolution matrix, which is solved by numerical linear algebra. However, this approach requires the prior information of the support of deconvolution filters. Using algebraic geometry and Gröbner bases, we find necessary and sufficient conditions for the existence of exact deconvolution FIR filters and propose simple algorithms to find these deconvolution filters. The main contribution of our work is to extend the previous Gröbner basis results on multidimensional multichannel deconvolution for polynomial or causal filters to general FIR filters. The proposed algorithms obtain a set of FIR deconvolution filters with a small number of nonzero coefficients (a desirable feature in the impulsive noise environment) and do not require the prior information of the support. Moreover, we provide a complete characterization of all exact deconvolution FIR filters, from which good FIR deconvolution filters under the additive white noise environment are found. Simulation results show that our approaches achieve good results under different noise settings.

Robust L-estimation based forms of signal transforms and time-frequency representations

The L-estimation based signal transforms and time-frequency (TF) representations are introduced by considering the corresponding minimization problems in the Huber (1981, 1998) estimation theory. The standard signal transforms follow as the maximum likelihood solutions for the Gaussian additive noise environment. For signals corrupted by an impulse noise, the median-based transforms produce robust estimates of the non-noisy signal transforms. When the input noise is a mixture of Gaussian and impulse noise, the L-estimation-based signal transforms can outperform other estimates. In quadratic and higher order TF analysis, the resulting noise is inherently a mixture of the Gaussian input noise and an impulse noise component. In this case, the L-estimation-based signal representations can produce the best results. These transforms and TF representations give the standard and the median-based forms as special cases. A procedure for parameter selection in the L-estimation is proposed. The theory is illustrated and checked numerically.

Speed estimation of mobile station in additive noise and rayleigh fading environments

In this paper, the speed of Mobile Station (MS) is estimated based on the derivative and correlation properties of received wave envelope in additive noise and Rayleigh fading channel environments. We derive the effects of noise and nonzero sampling interval on the accuracy of the speed estimation. In addition, we propose a method to correct the estimation errors due to the noise and sampling interval. We compare the proposed method with the speed estimation using level crossing rate. The proposed estimation method shows better performance than the estimation method using level crossing rate when there is background noise.

An improved (Auto:I, LSP:T) constrained iterative speech enhancement for colored noise environments

We illustrate how the (Auto:I, LSP:T) constrained iterative speech enhancement algorithm can be extended to provide improved performance in colored noise environments. The modified algorithm, referred to as noise adaptive (Auto:I, LSP:T), operates on subband signal components in which the terminating iteration is adjusted based on the a posteriori estimate of the signal-to-noise ratio (SNR) in each signal subband. The enhanced speech is formulated as a combined estimate from individual signal subband estimators. The algorithm is shown to improve objective speech quality in additive noise environments over the traditional constrained iterative (Auto:I, LSP:T) enhancement formulation.