White Noise Environment Research Articles

Array Signal Processing for Maximum Likelihood Direction-of-Arrival Estimation

Emitter Direction-of-Arrival (DOA) estimation is a fundamental problem in a variety of applications including radar, sonar, and wireless communications. The research has received considerable attention in literature and numerous methods have been proposed. Maximum Likelihood (ML) is a nearly optimal technique producing superior estimates compared to other methods especially in unfavorable conditions, and thus is of significant practical interest. This paper discusses in details the techniques for ML DOA estimation in either white Gaussian noise or unknown noise environment. Their performances are analyzed and compared, and evaluated against the theoretical lower bounds.

Speech Emotion Recognition under White Noise

Abstract Speaker‘s emotional states are recognized from speech signal with Additive white Gaussian noise (AWGN). The influence of white noise on a typical emotion recogniztion system is studied. The emotion classifier is implemented with Gaussian mixture model (GMM). A Chinese speech emotion database is used for training and testing, which includes nine emotion classes (e.g. happiness, sadness, anger, surprise, fear, anxiety, hesitation, confidence and neutral state). Two speech enhancement algorithms are introduced for improved emotion classification. In the experiments, the Gaussian mixture model is trained on the clean speech data, while tested under AWGN with various signal to noise ratios (SNRs). The emotion class model and the dimension space model are both adopted for the evaluation of the emotion recognition system. Regarding the emotion class model, the nine emotion classes are classified. Considering the dimension space model, the arousal dimension and the valence dimension are classified into positive regions or negative regions. The experimental results show that the speech enhancement algorithms constantly improve the performance of our emotion recognition system under various SNRs, and the positive emotions are more likely to be miss-classified as negative emotions under white noise environment.

Spectrogram analysis of frequency-hopping signals based on entropy measure

To analyze and estimate the parameters of Frequency-Hopping(FH) signals effectively,an adaptive time-frequency analytical method was proposed according to the characteristics of FH signal spectrogram and Wigner-Ville Didtribution(WVD).The width of window function for spectrogram was selected based on entropy measure so as to obtain optimal spectrogram representation of FH signal.The theoretical analysis and simulation results show that the spectrum analytical method based on entropy measure could give accurate estimation of the hopping cycle of FH signal under the white Gaussian noise environment of greater than 0 dB.Compared with Smoothed Pseudo WVD(SPWVD) and its adaptive method,the proposed method can effectively reduce the variance and improve the accuracy of parameter estimation at low Signal-to-Noise Ratio(SNR).While for long observation signal,it owns faster operation speed.

Carrier frequency offset estimation method for 2 × 1 MISO TDS-OFDM systems

Abstract In time-domain synchronous (TDS)-orthogonal frequency division multiplexing (OFDM) systems, a pseudo noise (PN) sequence is inserted instead of the cyclic prefix. The PN sequence is used not only as a guard interval but also as a training sequence for channel estimation and synchronization in the time domain. Recently, research studies on 2 × 1 multi input-single output (MISO) TDS-OFDM systems have been conducted, and different PN sequences (which are orthogonal to one another or cyclically shifted) are transmitted at each transmit antenna for channel estimation, which are modulated by binary phase shift keying in the same phase angle. However, when the absolute phase difference among the transmitted PN sequences is π, a PN sequence cancellation problem occurs, making the estimation of an accurate carrier frequency offset (CFO) difficult. In this paper, a CFO estimation method with the aid of PN sequences for 2 × 1 MISO TDS-OFDM systems is proposed. In the proposed method, the phase of the PN sequences at each antenna is rotated differently and transmitted to prevent a PN sequence-canceling problem. In addition, a CFO estimation scheme using channel state information is proposed to estimate an accurate CFO in time-varying channels. We show by computer simulations that the mean square error performance of the proposed method over an additive white Gaussian noise environment and time-varying Rayleigh channel is higher than that of the conventional method.

Iterative unbiased FIR state estimation: a review of algorithms

In this paper, we develop in part and review various iterative unbiased finite impulse response (UFIR) algorithms (both direct and two‐stage) for the filtering, smoothing, and prediction of time‐varying and time‐invariant discrete state‐space models in white Gaussian noise environments. The distinctive property of UFIR algorithms is that noise statistics are completely ignored. Instead, an optimal window size is required for optimal performance. We show that the optimal window size can be determined via measurements with no reference. UFIR algorithms are computationally more demanding than Kalman filters, but this extra computational effort can be alleviated with parallel computing, and the extra memory that is required is not a problem for modern computers. Under real‐world operating conditions with uncertainties, non‐Gaussian noise, and unknown noise statistics, the UFIR estimator generally demonstrates better robustness than the Kalman filter, even with suboptimal window size. In applications requiring large window size, the UFIR estimator is also superior to the best previously known optimal FIR estimators.

Bayesian Sparse Reconstruction Method of Compressed Sensing in the Presence of Impulsive Noise

The majority of existing recovery algorithms in the framework of compressed sensing are not robust to the impulsive noise. However, the impulsive noise is always present in the actual communication and signal processing system. In this paper, we propose a method named ‘Bayesian sparse reconstruction’ to recover the sparse signal from the measurement vector which is corrupted by the impulsive noise. The Bayesian sparse reconstruction method is composed of five parts, which are the preliminary detection of the location set of impulses, the impulsive noise fast relevance vector machine algorithm, the step of pruning, Bayesian impulse detection algorithm and the maximum a posteriori estimate of the sparse vector. The Bayesian sparse reconstruction method can achieve effective signal recovery in the presence of impulsive noise, depending on the mutual influence of the impulsive noise fast relevance vector machine algorithm, the step of pruning and the Bayesian impulse detection algorithm. Experimental results show that the Bayesian sparse reconstruction method is robust to the impulsive noise and effective in the additive white Gaussian noise environment.

Increasing convergence speed of FxLMS algorithm in white noise environment

A modification of the Filtered-x Least Mean Square (FxLMS) algorithm, aimed to reduce the influence of the secondary path and to increase the convergence speed in the white noise environment, is proposed. Secondary path disrupts the diagonality of the white noise autocorrelation matrix and increases its eigenvalue spread, causing a slower convergence speed of the FxLMS algorithm. We have modified the algorithm by adding the elements of the secondary path impulse response coefficients. In this way the resulting algorithm becomes a self-orthogonalizing FxLMS (SOFxLMS). As a consequence, the eigenvalue spread is reduced and the convergence speed is increased. The convergence analysis of the proposed algorithm and the simulation results confirm the performance improvement.

Marginal energy density over the low frequency range as a feature for voiced/non-voiced detection in noisy speech signals

In this paper, we present a pseudo Wigner–Ville distribution (PWVD) based novel method for the voiced/non-voiced (V/NV) detection in noisy speech signals. The energy distribution of the speech signal on the time–frequency plane is obtained by computing the PWVD coefficients of the analytic speech signal over the low frequency range (LFR). The marginal energy density with respect to time (MEDT) over the low frequency range (LFR) derived from the energy distribution of the speech signal on the time–frequency plane is used as a feature to provide the instantaneous V/NV detection. The experimental results on speech signals from the CMU-Arctic database under white, babble and vehicular noise environments taken from the NOISEX-92 database at various signal to noise ratio (SNR) are obtained to assess the performance of the proposed method. A significant performance improvement in the V/NV detection accuracy is obtained by the proposed method over the existing methods for the V/NV detection under the white noise and babble/vehicular noise environments, respectively.

Environmental Noise, Genetic Diversity and the Evolution of Evolvability and Robustness in Model Gene Networks

The ability of organisms to adapt and persist in the face of environmental change is accepted as a fundamental feature of natural systems. More contentious is whether the capacity of organisms to adapt (or “evolvability”) can itself evolve and the mechanisms underlying such responses. Using model gene networks, I provide evidence that evolvability emerges more readily when populations experience positively autocorrelated environmental noise (red noise) compared to populations in stable or randomly varying (white noise) environments. Evolvability was correlated with increasing genetic robustness to effects on network viability and decreasing robustness to effects on phenotypic expression; populations whose networks displayed greater viability robustness and lower phenotypic robustness produced more additive genetic variation and adapted more rapidly in novel environments. Patterns of selection for robustness varied antagonistically with epistatic effects of mutations on viability and phenotypic expression, suggesting that trade-offs between these properties may constrain their evolutionary responses. Evolution of evolvability and robustness was stronger in sexual populations compared to asexual populations indicating that enhanced genetic variation under fluctuating selection combined with recombination load is a primary driver of the emergence of evolvability. These results provide insight into the mechanisms potentially underlying rapid adaptation as well as the environmental conditions that drive the evolution of genetic interactions.

Variable separation solutions of the Wick-type stochastic Broer–Kaup system

With the help of Hermite transformation, we obtain variable separation solutions of the Wick-type stochastic Broer–Kaup system in the white noise environment. These variable separation solutions are more general than travelling solutions reported in the literature. To illustrate the novelty of variable separation solutions, we present some localized structures with chaotic behaviors.

Effects of White Noises on Gait Ability of Hemiplegic Patients during Circuit Balance Training

This study examines the effects of different environments on the application of hemiplegia patients circuit balance training. Group 1 performed circuit balance training without any auditory intervention Group 2 performed training in noiseless environments and Group 3 performed training in white noise environments. First, among lower extremity muscular strength evaluation items, maximum activity time(MAT) was not significantly different(p>.05). Maximum muscle strength(MMS) increased significantly in Group 3(p<.01), there was no significant difference in MMS among the groups. Average muscle strength(AMS) indexes also significantly increased in Group 3(p<.01), there was no significant difference in AMS among the groups. Second, among balancing ability evaluation items, Berg's balance scale(BBS) scores significantly increased in all groups(p<.05), BBS scores were significantly difference among the groups. Based on the results, Group 1, 2 and Group 1, 3 showed significant increases (p<.05). Functional reach test(FRT) values significantly increased in Group 2, 3(p<.05), and there was no significant difference in FRT values among the groups. Timed up and go(TUG) test values significantly decreased in Group 2, 3(p<.05), and there was no significant difference in TUG test values among the groups. Third, among walking speed evaluation items, the time required to walk 10m significantly decreased in all groups(p<.05), and there was no significant difference in the values among the groups. Average walking speeds showed significant increases in Group 1, 3(p<.05), and there was no significant difference in the values among the groups. Based on the results of this study, noise environments should be improved by either considering auditory interventions and noiseless environments, or by ensuring that white noise environments facilitate the enhancement of balancing ability.

DWT and LPC based feature extraction methods for isolated word recognition

In this article, new feature extraction methods, which utilize wavelet decomposition and reduced order linear predictive coding (LPC) coefficients, have been proposed for speech recognition. The coefficients have been derived from the speech frames decomposed using discrete wavelet transform. LPC coefficients derived from subband decomposition (abbreviated as WLPC) of speech frame provide better representation than modeling the frame directly. The WLPC coefficients have been further normalized in cepstrum domain to get new set of features denoted as wavelet subband cepstral mean normalized features. The proposed approaches provide effective (better recognition rate), efficient (reduced feature vector dimension), and noise robust features. The performance of these techniques have been evaluated on the TI-46 isolated word database and own created Marathi digits database in a white noise environment using the continuous density hidden Markov model. The experimental results also show the superiority of the proposed techniques over the conventional methods like linear predictive cepstral coefficients, Mel-frequency cepstral coefficients, spectral subtraction, and cepstral mean normalization in presence of additive white Gaussian noise.

Optimization of cooperative spectrum sensing with sensing user selection in cognitive radio networks

Cooperative spectrum sensing (CSS) can improve the spectrum sensing performance by introducing spatial diversity in cognitive radio networks (CRNs). However, such cooperation also introduces the delay for reporting sensing data. Conventional cooperation scheme assumes that the cooperative secondary users (SUs) report their local sensing data to the fusion center sequentially. This causes the reporting delay to increase with the number of the cooperative SUs, and ultimately affects the performance of CSS. In this article, we consider the reporting delay and formulate the optimization problem of CSS with sensing user selection to maximize the average throughput of the CRN in both the additive white Gaussian noise (AWGN) environment and the Rayleigh fading environment. It is shown that selecting all the SUs within the CRN to cooperate might not achieve the maximal average throughput. In particular, for the AWGN environment, the sensing user selection scheme is equivalent to selecting the optimal number of cooperative SUs due to all the SUs having the same instantaneous detection signal-to-noise ratio (SNR). For the Rayleigh fading environment, the maximal average throughput is achieved by selecting a certain number of cooperative SUs with the highest instantaneous detection SNRs to cooperate. Finally, computer simulations are presented to demonstrate that the average throughput of the CRN can be maximized through the optimization.

Three-Dimensional Modulation Formats with Constant Power for Optical Communications

In this paper, a new method of constructing three-dimensional modulation formats with constant power is introduced. Constellations designed by the method have slightly larger minimum Euclidean distances (MEDs) than the conventional ones. No repetitive algorithm to maximize MED is used so that the new method has little computational complexity. Since signal points in the new formats are distributed regularly and symmetrically, an error control coding with systematic set-partition is applicable. We also present theoretical symbol error probability (SEP) of the new constellations in an additive white Gaussian noise environment, and demonstrate that the theoretical results are accurate. As the new modulation formats have almost the same or slightly lower SEPs than the conventional ones, they are appropriate for implementing a highly reliable optical communication system.

Exponential Sampling: A Gibbs Phenomena Removal Model for Finite Rate of Innovation Sampling Framework

We propose an exponential approximating function as a sampling kernel with finite rate of innovation. The performance of reconstruct non-bandlimited signals from its low frequency components would inevitably induce Gibbs phenomenon. This paper establishes the theoretical model on relationship between sampling kernel filter and parametric reconstruction method of non-bandlimited signals, and designs a new window function exponential sampling kernel filter to removal Gibbs Influence. Simulation results show that, compared to Sinc sampling kernel filter, the reconstruction ability of exponential filter based finite rate sampling system is improved under the white Gaussian noise environment.

Using Ryodoraku Measurement to Evaluate the Impact of Environmental Noise on Human Physiological Response

According to relevant studies on the influence of indoor sound environment on human responses, long-term exposure to excessive indoor noise can have a significant effect on human physiological responses, such as heart rate variability, electroencephalography and blood pressure. Moreover, it may cause tinnitus, palpitation and headache. In order to find out the impact of noise environment on human physiological response changes, this study undertook experiments in a Lab with a precise control of environmental factors and based on the viewpoint of meridian of traditional Chinese medicine, the changes in human autonomic nervous response and the degree of effect on corresponding meridians after 45 min stimulation of white noise environment at 40 dB (A), 50 dB (A), 60 dB (A) and 70 dB (A) sound pressure levels, were evaluated. The analysis of the experimental results found that, the human Heart Constrictor meridians, Heart meridians and Triple Heater meridians were related to pericardium, blood vessel and blood circulation system; and are likely to be influenced by the noise environment stimulation. When the noise sound pressure level exceeded 60 dB (A), the human Bladder meridians and Gall Bladder meridians related to ears, eyes, shoulder joints, digestive system and endocrine system could be influenced significantly.

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.

Analytical solutions for the stochastic Gardner equation

With the help of the Hermite transformation, some analytical solutions of the stochastic Gardner equation in the white noise environment are obtained using the exp-function method.

A series of exact solutions for (2 + 1)-dimensional Wick-type stochastic generalized Broer–Kaup system via a modified variable-coefficient projective Riccati equation mapping method

A modified variable-coefficient projective Riccati equation mapping method is applied to (2 + 1)-dimensional Wick-type stochastic generalized Broer–Kaup system. With the help of Hermit transformation, we obtain a series of new exact stochastic solutions to the stochastic Broer–Kaup system in the white noise environment.

Heterogeneous response dynamics in retinal ganglion cells: the interplay of predictive coding and adaptation.

To make efficient use of their limited signaling capacity, sensory systems often use predictive coding. Predictive coding works by exploiting the statistical regularities of the environment--specifically, by filtering the sensory input to remove its predictable elements, thus enabling the neural signal to focus on what cannot be guessed. To do this, the neural filters must remove the environmental correlations. If predictive coding is to work well in multiple environments, sensory systems must adapt their filtering properties to fit each environment's statistics. Using the visual system as a model, we determine whether this happens. We compare retinal ganglion cell dynamics in two very different environments: white noise and natural. Because natural environments have more power than that of white noise at low temporal frequencies, predictive coding is expected to produce a suppression of low frequencies and an enhancement of high frequencies, compared with the behavior in a white-noise environment. We find that this holds, but only in part. First, predictive coding behavior is not uniform: most on cells manifest it, whereas off cells, on average, do not. Overlaid on this nonuniformity between cell classes is further nonuniformity within both cell classes. These findings indicate that functional considerations beyond predictive coding play an important role in shaping the dynamics of sensory adaptation. Moreover, the differences in behavior between on and off cell classes add to the growing evidence that these classes are not merely homogeneous mirror images of each other and suggest that their roles in visual processing are more complex than expected from the classic view.