Effect Of Additive Noise Research Articles

Robust Speaker Identification in Noisy and Reverberant Conditions

Robustness of speaker recognition systems is crucial for real-world applications, which typically contain both additive noise and room reverberation. However, the combined effects of additive noise and convolutive reverberation have been rarely studied in speaker identification (SID). This paper addresses this issue in two phases. We first remove background noise through binary masking using a deep neural network classifier. Then we perform robust SID with speaker models trained in selected reverberant conditions, on the basis of bounded marginalization and direct masking. Evaluation results show that the proposed system substantially improves SID performance over related systems in a wide range of reverberation time and signal-to-noise ratios.

A Rigorous Analysis of Least Squares Sine Fitting Using Quantized Data: The Random Phase Case

This paper considers least square (LS) based estimation of the amplitude and square amplitude of a quantized sine wave, done by considering random initial record phase. Using amplitude- and frequency-domain modeling techniques, it is shown that the estimator is inconsistent, biased, and has a variance that may be underestimated if the simple model of quantization is applied. The effects of both sine wave offset values and additive Gaussian noise are considered. General estimator properties are derived, without making simplifying assumptions on the role of the quantization process, to allow assessment of measurement uncertainty, when this LS procedure is used.

A modular theory of multisensory integration for motor control

To control targeted movements, such as reaching to grasp an object or hammering a nail, the brain can use divers sources of sensory information, such as vision and proprioception. Although a variety of studies have shown that sensory signals are optimally combined according to principles of maximum likelihood, increasing evidence indicates that the CNS does not compute a single, optimal estimation of the target's position to be compared with a single optimal estimation of the hand. Rather, it employs a more modular approach in which the overall behavior is built by computing multiple concurrent comparisons carried out simultaneously in a number of different reference frames. The results of these individual comparisons are then optimally combined in order to drive the hand. In this article we examine at a computational level two formulations of concurrent models for sensory integration and compare this to the more conventional model of converging multi-sensory signals. Through a review of published studies, both our own and those performed by others, we produce evidence favoring the concurrent formulations. We then examine in detail the effects of additive signal noise as information flows through the sensorimotor system. By taking into account the noise added by sensorimotor transformations, one can explain why the CNS may shift its reliance on one sensory modality toward a greater reliance on another and investigate under what conditions those sensory transformations occur. Careful consideration of how transformed signals will co-vary with the original source also provides insight into how the CNS chooses one sensory modality over another. These concepts can be used to explain why the CNS might, for instance, create a visual representation of a task that is otherwise limited to the kinesthetic domain (e.g., pointing with one hand to a finger on the other) and why the CNS might choose to recode sensory information in an external reference frame.

Pulse Bifurcations in Stochastic Neural Fields

We study the effects of additive noise on traveling pulse solutions in spatially extended neural fields with linear adaptation. Neural fields are evolution equations with an integral term characterizing synaptic interactions between neurons at different spatial locations of the network. We introduce an auxiliary variable to model the effects of local negative feedback and consider random fluctuations by modeling the system as a set of spatially extended Langevin equations whose noise term is a $Q$-Wiener process. Due to the translation invariance of the network, neural fields can support a continuum of spatially localized bump solutions that can be destabilized by increasing the strength of the adaptation, giving rise to traveling pulse solutions. Near this criticality, we derive a stochastic amplitude equation describing the dynamics of these bifurcating pulses when the noise and the deterministic instability are of comparable magnitude. Away from this bifurcation, we investigate the effects of additive ...

Large deviations for nonlocal stochastic neural fields.

We study the effect of additive noise on integro-differential neural field equations. In particular, we analyze an Amari-type model driven by a Q-Wiener process, and focus on noise-induced transitions and escape. We argue that proving a sharp Kramers’ law for neural fields poses substantial difficulties, but that one may transfer techniques from stochastic partial differential equations to establish a large deviation principle (LDP). Then we demonstrate that an efficient finite-dimensional approximation of the stochastic neural field equation can be achieved using a Galerkin method and that the resulting finite-dimensional rate function for the LDP can have a multiscale structure in certain cases. These results form the starting point for an efficient practical computation of the LDP. Our approach also provides the technical basis for further rigorous study of noise-induced transitions in neural fields based on Galerkin approximations.Mathematics Subject Classification (2000): 60F10, 60H15, 65M60, 92C20.

Back-and-forth micromotion of aqueous droplets in a dc electric field

Recently, it was reported that an aqueous droplet in an oil phase exhibited rhythmic back-and-forth motion under stationary dc voltage on the order of 100 V. Here, we demonstrate that the threshold voltage for inducing such oscillation is successfully decreased to the order of 10 V through downsizing of the experimental system. Notably, the threshold electric field tends to decrease with a nonlinear scaling relationship accompanied by the downsizing. We derive a simple theoretical model to interpret the system size dependence of the threshold voltage. This model equation suggests the unique effect of additional noise, which is qualitatively characterized as a coherent resonance by an actual experiment as a kind of coherent resonance. Our result would provide insight into the construction of micrometer-sized self-commutating motors and actuators in microfluidic and micromechanical devices.

A time-synchronous histogram equalization for noise robust speech recognition

The histogram equation (HEQ) technique is commonly adopted for feature space normalization in speech recognition systems. In this technique, a transform function is calculated directly from the histograms of both training and test data, and the nonlinear effects of additive noise are compensated. In order to estimate the transform function accurately, a certain amount of data are required. However, this is not suitable for real-time application because at least several seconds of evaluation data need to be accumulated before the transform function can be calculated. This means that the system cannot start the recognition process until the end of utterance. In this research, we aim to develop a new speech recognition method based on the HEQ technique for real-time processing. This method is called “time-synchronous frame-weighted HEQ (ts-FHEQ).” In the time-synchronous decoding, lack of data for estimating the histogram becomes a major problem. To resolve this problem, we introduce a frame weighting approach, where the degree of transform is controlled according to the number of data frames. Our speech recognition experiments verified that the proposed technique shows good performance and achieves substantial reduction of calculation time.

Bifurcations of emerging patterns in the presence of additive noise

A universal description of the effects of additive noise on super- and subcritical spatial bifurcations in one-dimensional systems is theoretically, numerically, and experimentally studied. The probability density of the critical spatial mode amplitude is derived. From this generalized Rayleigh distribution we predict the shape of noisy bifurcations by means of the most probable value of the critical mode amplitude. Comparisons with numerical simulations are in quite good agreement for cubic or quintic amplitude equations accounting for stochastic supercritical bifurcation and for cubic-quintic amplitude equation accounting for stochastic subcritical bifurcation. Experimental results obtained in a one-dimensional Kerr-like slice subjected to optical feedback confirm the analytical expression prediction for the supercritical bifurcation shape.

A novel ultra low power ASK demodulator for a passive UHF RFID tag compatible with C1 G2 EPC standard protocol

In order to reduce the power consumption of RFID tags and increase the reading range of RFID systems, this paper proposes an ASK demodulator that uses a new approach to reduce the threshold voltage of diode connected MOS transistors as an obstacle in the design of the envelope detector. Also, an ultra low power comparator is used for further power reduction. This circuit has been simulated in a 0.18 μm CMOS technology to satisfy EPC Class 1 Generation 2 standard protocol emphasizing on the reduction of power consumption. The proposed circuit can correctly demodulate the minimum input RF signal amplitude of 180 mV for modulation depths of 55---100 % with 40---160 kb/s data rates. A total power consumption of less than 290 nW is achieved at a 1.2 V power supply. Effects of the input signal additive white noise as well as the process and temperature variations on the signal demodulation is also investigated in this paper.

Comparing Predictive Abilities of Three Visible-Near Infrared Spectrophotometers for Soil Organic Carbon and Clay Determination

Due to advances in optical technology, a wide range of spectrometers is available. Recent interests in soil global libraries and sensor fusion presents a challenge with respect to combining data from different instrumentation. Little research, however, has been done on the comparison of visible-near infrared (vis-NIR) spectrometers for soil characterisation. There is a need for more work on the effects of scanning strategies and use of different soil instrumentation. We compared three vis-NIR spectrometers with varying resolution, signal-to-noise ratios and spectral range. Their performance was evaluated based on spectra collected from 194 Danish top soils and used to determine soil organic carbon (SOC) and clay content. Scanning procedures for the three spectrophotometers where done according to uniform laboratory protocols. Soil organic carbon and clay calibrations were performed using PLS regression. One third of the data set was used as an independent test set. A range of spectral preprocessing methods was applied in search of model improvement. Validation for SOC content using an independent data set derived from all three spectrophotometers provided values of RMSEP between 0.45% and 0.52%, r2=0.42–0.59 and RPD = 1.2–1.4. Clay content was predicted with a higher precision resulting in RMSEP values between 2.6% and 2.9%, r2 = 0.71–0.77 and RPD values in the range from 2.2 to 2.5. No substantial differences in the prediction accuracy were found for the three spectrometers, although there was a tendency that, in the tradeoff between noise and resolution, low noise was the more important for SOC and clay predictions. The application of different spectral preprocessing procedures did not generate important improvements of the calibration models either. Additionally, data simulation analysis, including resampling to a coarser resolution and addition of noise, was performed. No, or very little, effect of sampling resolution and additional noise on the performance of the spectrophotometers was reported. The results from this study showed that, as long as strict laboratory scanning protocols were followed, no significant differences in constituent determination were found, despite differences in spectral range, spectral resolution, spectral sampling intervals and sample presentation methods. The differences in predictive abilities between the spectrometers were mostly due to differences in spectral range.

Bipolar Multiplexed Pulse Width Modulation

This paper presents generation and detection of two-channel bipolar multiplexed pulse width modulated signal. The positive half cycles of a polar triangular wave of frequency fc are modulated in amplitude by one message signal and the negative half cycles are modulated in amplitude by another message signal. The two modulated polar triangular waves, each carrying a distinct message signal, are added to generate a carrier whose positive envelope resembles one message signal while negative envelope resembles second message signal. This is a bipolar division multiplexed (BDM) signal in which the initial carrier considered is polar triangular wave. The BDM signal is passed simultaneously through positive and negative clipper circuits. The clippers output unipolar trapezoidal waveforms of opposite polarity. Each polar trapezoidal waveform is differentiated to generate a sequence of positive and negative pulses. Positive pulses alone are considered from the pulse train obtained by differentiating positive trapezoidal signal while negative pulses alone are considered from the pulse train obtained by differentiating the negative trapezoidal signal. The two sets of pulses are added to form Bipolar multiplexed pulse width modulated signal. The message signal recovery involves demultiplexing the pulse trains, clipping their amplitude levels to avoid the effect of additive noise followed by low pass filtering. The proposed scheme offers multiplexing feature with inherent synchronizing gap resulting in no requirement for synchronizing pulses. The proposed scheme requires more bandwidth and hence better noise performance when compared to conventional multiplexed PWM scheme. The proposed scheme is better suited when the dynamic range of the message signal is in the order of a few tens of millivolts.

Reconstruction of Noisy Electromagnetic Fields by Means of Compressive Sensing Theory

This paper deals with the emerging compressive sensing theory applied to reconstruction of the spatial distribution of a magnetic/electric field corrupted by additive noise. A typical application could be monitoring of the electromagnetic environment of a wide area where an electromagnetic source or susceptible electric/electronic apparatus are located. To this end, wireless field sensors can be assumed to be deployed over the monitored area and used to provide spatial samples of the field. The main advantages offered by compressive sensing include a number of sensors much smaller than the number foreseen by the traditional Shannon sampling theory, and the possibility to resort to nonuniform distribution of the sensors. A specific numerical analysis is devoted to investigate the effects of additive noise introduced by wireless technology, including quantization noise featuring low-cost sensors, environment and electronic noise.

Effect of interpolation filter in digital quadrature amplitude modulation signal resampling

In this study, a closed-form equation is derived which relates the mean-squared error (MSE) to the frequency response of the interpolation filter. Effects of additive white Gaussian noise and timing jitter on the MSE are accounted for in the equation. By using properties of the Nyquist signalling pulse, a simple approximation to the MSE equation is obtained, which is found to be accurate over the range of excess bandwidths commonly used in quadrature amplitude modulation receivers.

General Noise-Perturbed Superior Julia Sets

The aim of this paper is to offer an integrated approach to study the additive and multiplicative noises with respect to perturbations in superior Julia sets. External and internal perturbations in superior Julia sets are analyzed under the mixed effect of additive and multiplicative noises.

Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings

Rolling element bearings are frequently used in rotary machinery, but they are also fragile mechanical parts. Hence, exact condition monitoring and fault diagnosis for them plays an important role in ensuring machinery's reliable running. Timely diagnosis of early bearing faults is desirable, but the early fault signatures are easily submerged in noise.In this paper, Wigner–Ville spectrum based on cyclic spectral density (CSWVS for a brief notation) is studied, which is able to represent the cyclostationary signals while reducing the masking effect of additive stationary noise. Both simulations and experiments show that CSWVS is a noise resistant time frequency analysis technique for extracting bearing fault patterns, when bearing signals are under influences of random noise and gear vibrations. The 3-D feature of the CSWVS is proved useful in extracting bearing fault pattern from gearbox vibration signals, where bearing signals are affected by gear meshing vibration and noise. Besides, CSWVS utilizes the second order cyclostationary property of the vibration signals produced by bearing distributed fault, and clearly extracts its fault features, which cannot be extracted by envelope analysis.To quantitatively describe the extent of bearing fault, Renyi information encoded in the time frequency diagram of CSWVS is studied. It is shown to be a more sensitive index to reflect bearing performance degradation, compared with the spectral entropy (SE), squared envelope spectrum entropy (SESE) and Renyi informations for WVD, PWVD, especially when SNR is low.

Additive noise effects in active nonlinear spatially extended systems

We examine the effects of pure additive noise on spatially extended systems with quadratic nonlinearities. We develop a general multi-scale theory for such systems and apply it to the Kuramoto–Sivashinsky equation as a case study. We first focus on a regime close to the instability onset (primary bifurcation), where the system can be described by a single dominant mode. We show analytically that the resulting noise in the equation describing the amplitude of the dominant mode largely depends on the nature of the stochastic forcing. For a highly degenerate noise, in the sense that it is acting on the first stable mode only, the amplitude equation is dominated by a pure multiplicative noise, which in turn induces the dominant mode to undergo several critical state transitions and complex phenomena, including intermittency and stabilisation, as the noise strength is increased. The intermittent behaviour is characterised by a power-law probability density and the corresponding critical exponent is calculated rigorously by making use of the first-passage properties of the amplitude equation. On the other hand, when the noise is acting on the whole subspace of stable modes, the multiplicative noise is corrected by an additive-like term, with the eventual loss of any stabilised state. We also show that the stochastic forcing has no effect on the dominant mode dynamics when it is acting on the second stable mode. Finally, in a regime which is relatively far from the instability onset so that there are two unstable modes, we observe numerically that when the noise is acting on the first stable mode, both dominant modes show noise-induced complex phenomena similar to the single-mode case.

The effects of observational correlated noises on multifractal detrended fluctuation analysis

We have numerically investigated the effects that observational correlated noises have on the generalized Hurst exponents, h(q), estimated by using the multifractal generalization of detrended fluctuation analysis (MF-DFA). More precisely, artificially generated stochastic binomial multifractals with increased amount of colored noises were analyzed via MF-DFA. It has been recently shown that for moderate additions of white noise, the generalized Hurst exponents are significantly underestimated for q<2 and they are nearly unchanged for q≥2 [J. Ludescher, M.I. Bogachev, J.W. Kantelhardt, A.Y. Schumann, A. Bunde, On spurious and corrupted multifractality: the effects of additive noise, short- term memory and periodic trends, Physica A 390 (2011) 2480–2490]. In this paper, we have found that h(q) with q≥2 are also affected when correlated noises are considered. This is due to the fact that the spurious correlations influence the scaling behaviors associated to large fluctuations. The results obtained are significant for practical situations, where noises with different correlations are inherently present.

Emergent bistability: Effects of additive and multiplicative noise

Positive feedback and cooperativity in the regulation of gene expression are generally considered to be necessary for obtaining bistable expression states. Recently, a novel mechanism of bistability termed emergent bistability has been proposed which involves only positive feedback and no cooperativity in the regulation. An additional positive feedback loop is effectively generated due to the inhibition of cellular growth by the synthesized proteins. The mechanism, demonstrated for a synthetic circuit, may be prevalent in natural systems also as some recent experimental results appear to suggest. In this paper, we study the effects of additive and multiplicative noise on the dynamics governing emergent bistability. The calculational scheme employed is based on the Langevin and Fokker-Planck formalisms. The steady state probability distributions of protein levels and the mean first passage times are computed for different noise strengths and system parameters. In the region of bistability, the bimodal probability distribution is shown to be a linear combination of a lognormal and a Gaussian distribution. The variances of the individual distributions and the relative weights of the distributions are further calculated for varying noise strengths and system parameters. The experimental relevance of the model results is also pointed out.

Suprathreshold stochastic resonance of a non-linear multilevel threshold neuronal networks system

Suprathreshold stochastic resonance can be used to explain some biologic phenomena. In this paper, the suprathreshold stochastic resonance of a non-linear multilevel threshold neuronal network system is studied. The expression of the mutual information is derived, and the effects of the noise intensity and the system parameter on mutual information are discussed. The numerical computation results show that in the process of information transmission the selection of the threshold is very important according to the different effects of additive noise and multiplicative noise on system. Moreover, we also find that the more the number of devices, the more apparent the phenomenon of suprathreshold stochastic resonance is.

Accuracy of the Normalized Frequency Estimation of a Discrete-Time Sine-Wave by the Energy-Based Method

This paper investigates the effect of algorithm error, spectral interference, and additive wideband noise on the accuracy of the normalized frequency estimation provided by the Energy-Based Method (EBM) in the case of a discrete-time sine-wave. The statistical efficiency of the EBM estimator with respect to the unbiased Cramér-Rao Lower Bound (CRLB) is determined. The accuracy of the derived expressions is confirmed by means of computer simulations. Moreover, the expression of the rms error of the normalized frequency estimate is derived. Finally, the performance of the normalized frequency estimators provided by the EBM and other state-of-the-art methods are compared by means of both simulations and experimental results.