Showing papers on "Noise measurement published in 2006"

Advanced Digital Signal Processing and Noise Reduction

Abstract: Contents Symbols Abbreviations 1 Introduction 1.1 Signals, Noise and Information 1.2 Signal Processing Methods 1.3 Applications of Digital Signal Processing 1.4 A Review of Sampling and Quantisation 1.5 Summary Bibliography 2 Noise and Distortion 2.1 Introduction 2.2 White Noise 2.3 Coloured Noise Pink Noise and Brown Noise 2.4 Impulsive and Click Noise 2.5 Impulsive and Click Noise 2.6 Thermal Noise 2.7 Shot Noise 2.8 Flicker (I/f) Noise 2.9 Burst Noise 2.10 Electromagnetic (Radio) Noise 2.11 Channel Distortions 2.12 Echo and Multi-path Reflections 2.13 Modelling Noise 2.14 Summary Bibliography 3 Information Theory and Probability Models 3.1 Introduction: Probability and Information Models 3.2 Random Processes 3.3 Probability Models 3.4 Information Models 3.5 Stationary and Non-stationary Processes 3.6 Expected Values of a Process 3.7 Some Useful Classes of Random Processes 3.8 Transformation of a Random Process 3.9 Search Engines: Citation Ranking 3.10 Summary Bibliography 4 Baseyian Inference 4.1 Bayesian Estimation Theory: Basic Definitions 4.2 Bayesian Estimation 4.3 The Estimate-Maximise Method 4.4 Cramer-Rao Bound on the Minimum Estimator Variance 4.5 Design of Gaussian Mixture Models 4.6 Bayesian Classification 4.7 Modeling the Space of a Random Process 4.8 Summary Bibliography 5 Hidden Markov Models 5.1 Statistical Models for Non-Stationary Processes 5.2 Hidden Markov Models 5.3 Training Hidden Markov Models 5.4 Decoding of Signals Using Hidden Markov Models 5.5 HMM In DNA and Protein Sequence Modelling 5.6 HMMs for Modelling Speech and Noise 5.7 Summary Bibliography 6 Least Square Error Wiener-Kolmogorov Filters 6.1 Least Square Error Estimation: Wiener-Kolmogorov Filter 6.2 Block-Data Formulation of the Wiener Filter 6.3 Interpretation of Wiener Filters as Projection in Vector Space 6.4 Analysis of the Least Mean Square Error Signal 6.5 Formulation of Wiener Filters in the Frequency Domain 6.6 Some Applications of Wiener Filters 6.7 Implementation of Wiener Filters 6.8 Summary Bibliography 7 Adaptive Filters, Kalman, RLS, LMS 7.1 Introduction 7.2 State-Space Kalman Filter 7.3 Extended Kalman Filter 7.4 Unscented Kalman Filter 7.5 Sample-Adaptive Filters 7.6 Recursive Least Square(RLS) Adaptive Filters 7.7 The Steepest-Descent Method 7.8 The LMS Filter 7.9 Summary Bibliography 8 Linear Prediction Models 8.1 Linear Prediction Coding 8.2 Forward, Backward and Lattice Predictors 8.3 Short-term and Long-Term Linear Predictors 8.4 MAP Estimation of Predictor Coefficients 8.5 Formant-Tracking LP Models 8.6 Sub-Band Linear Prediction 8.7 .i.Signal Restoration Using Linear Prediction Models 8.8 Summary Bibliography 9 Eigenvalue Analysis and Principal Component Analysis 9.1 Introduction 9.2 Eigen Analysis 9.3 Principal Component Analysis 9.4 Summary Bibliography 10 Power Spectrum Analysis 10.1 Power Spectrum and Correlation 10.2 Fourier Series: Representation of Periodic Signals 10.3.3 Energy-Spectral Density and Power-Spectral Density 10.3 Fourier Transform: Representation of Aperiodic Signals 10.4 Non-Parametric Power Spectrum Estimation 10.5 Model-Based Power Spectral Estimation 10.6 High Resolution Spectral Estimation Based on Subspace Eigen-Analysis 10.7 Summary Bibliography 11. Interpolation - Replacement of Lost Samples 11.1 Introduction 11.2 Model-Based Interpolation 11.3 Model-Based Interpolation 11.4 Summary Bibliography 12 Signal Enhancement via Spectral Amplitude Estimation 12.1Introduction 12.2 Spectral Representation of Noisy Signals 12.3 Vector Representation of Spectrum of Noisy Signals 12.4 Spectral Subtraction 12.5 Bayesian MMSE Spectral Amplitude Estimation 12.6 Estimation of Signal to Noise Ratios 12.7 Application to Speech Restoration and Recognition 12.8 Summary Bibliography 13 Impulsive Noise: Modelling, Detection and Removal 13.1 Impulsive Noise 13.2 Autocorrelation and Power Spectrum of Impulsive Noise 13.3 Probability Models for Impulsive Noise 13.4 Impulse contamination, Signal to Impulsive Noise Ratio 13.5 Median Filters 13.6 Impulsive Noise Removal Using Linear Prediction Models 13.7 Robust Parameter Estimation 13.8 Restoration of Archived Gramophone Records 13.9 Summary Bibliography 14 Transient Noise Pulses 14.1 Transient Noise Waveforms 14.2 Transient Noise Pulse Models 14.3 Detection of Noise Pulses 14.4 Removal of Noise Pulse Distortions 14.5 Summary Bibliography 15 Echo Cancellation 15.1 Introduction: Acoustic and Hybrid.i.Hybrid Echoes 15.2 Echo Return Time: The Sources of Delay in Communication Networks 15.3 Telephone Line Hybrid Echo 15.4 Hybrid Echo Suppression 15.5 .i.Adaptive Echo Cancellation 15.6 Acoustic .i.Echo 15.7 .i.Sub-band Acoustic Echo Cancellation 15.8 .i. Echo Cancellation with Linear Prediction Pre-whitening 15.9 Multiple-Input Multiple-Output (MIMO) Acoustic Echo Cancellation 15.10 Summary Bibliography 16 Channel Equalisation and Blind Deconvolution 16.1 Introduction 16.2 Blind-Deconvolution Using Channel Input Power Spectrum 16.3 Equalisation Based on Linear Prediction Models 16.4 Bayesian Blind Deconvolution and Equalisation 16.5 Blind Equalisation for Digital Communication Channels 16.6 Equalisation Based on Higher-Order Statistics 16.7 Summary 16.8 Bibliography 17 Speech Enhancement: Noise Reduction, Bandwidth Extension and Packet Replacement 17.1 An Overview of Speech Enhancement in Noise 17.2 Single-Input Speech Enhancement Methods 17.3 Speech Bandwidth Extension 17.4 Interpolation of Lost Speech Segments 17.5 Multiple-Input Speech Enhancement Methods 17.6 Speech Distortion Measurements 17.7 Summary 17.8 Bibliography 18 Multiple-Input Multiple-Output Systems, Independent Component Analysis 18.1 Introduction 18.2 MIMO Signal Propagation and Mixing Models 18.3 Independent Component Analysis 18.4 Summary Bibliography 19 Signal Processing in Mobile Communication 19.1 Introduction to Cellular Communication 19.2 Communication Signal Processing in Mobile Systems 19.3 Noise, Capacity and Spectral Efficiency 19.4 Multi-path and Fading in Mobile Communication 19.5 Smart Beam-forming Antennas 19.6 Summary Bibliography Index

Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing

Abstract: In this paper, a quaternion based extended Kalman filter (EKF) is developed for determining the orientation of a rigid body from the outputs of a sensor which is configured as the integration of a tri-axis gyro and an aiding system mechanized using a tri-axis accelerometer and a tri-axis magnetometer. The suggested applications are for studies in the field of human movement. In the proposed EKF, the quaternion associated with the body rotation is included in the state vector together with the bias of the aiding system sensors. Moreover, in addition to the in-line procedure of sensor bias compensation, the measurement noise covariance matrix is adapted, to guard against the effects which body motion and temporary magnetic disturbance may have on the reliability of measurements of gravity and earth's magnetic field, respectively. By computer simulations and experimental validation with human hand orientation motion signals, improvements in the accuracy of orientation estimates are demonstrated for the proposed EKF, as compared with filter implementations where either the in-line calibration procedure, the adaptive mechanism for weighting the measurements of the aiding system sensors, or both are not implemented.

A switching median filter with boundary discriminative noise detection for extremely corrupted images

Abstract: A novel switching median filter incorporating with a powerful impulse noise detection method, called the boundary discriminative noise detection (BDND), is proposed in this paper for effectively denoising extremely corrupted images. To determine whether the current pixel is corrupted, the proposed BDND algorithm first classifies the pixels of a localized window, centering on the current pixel, into three groups-lower intensity impulse noise, uncorrupted pixels, and higher intensity impulse noise. The center pixel will then be considered as "uncorrupted," provided that it belongs to the "uncorrupted" pixel group, or "corrupted." For that, two boundaries that discriminate these three groups require to be accurately determined for yielding a very high noise detection accuracy-in our case, achieving zero miss-detection rate while maintaining a fairly low false-alarm rate, even up to 70% noise corruption. Four noise models are considered for performance evaluation. Extensive simulation results conducted on both monochrome and color images under a wide range (from 10% to 90%) of noise corruption clearly show that our proposed switching median filter substantially outperforms all existing median-based filters, in terms of suppressing impulse noise while preserving image details, and yet, the proposed BDND is algorithmically simple, suitable for real-time implementation and application.

New insights into the noise reduction Wiener filter

Abstract: The problem of noise reduction has attracted a considerable amount of research attention over the past several decades. Among the numerous techniques that were developed, the optimal Wiener filter can be considered as one of the most fundamental noise reduction approaches, which has been delineated in different forms and adopted in various applications. Although it is not a secret that the Wiener filter may cause some detrimental effects to the speech signal (appreciable or even significant degradation in quality or intelligibility), few efforts have been reported to show the inherent relationship between noise reduction and speech distortion. By defining a speech-distortion index to measure the degree to which the speech signal is deformed and two noise-reduction factors to quantify the amount of noise being attenuated, this paper studies the quantitative performance behavior of the Wiener filter in the context of noise reduction. We show that in the single-channel case the a posteriori signal-to-noise ratio (SNR) (defined after the Wiener filter) is greater than or equal to the a priori SNR (defined before the Wiener filter), indicating that the Wiener filter is always able to achieve noise reduction. However, the amount of noise reduction is in general proportional to the amount of speech degradation. This may seem discouraging as we always expect an algorithm to have maximal noise reduction without much speech distortion. Fortunately, we show that speech distortion can be better managed in three different ways. If we have some a priori knowledge (such as the linear prediction coefficients) of the clean speech signal, this a priori knowledge can be exploited to achieve noise reduction while maintaining a low level of speech distortion. When no a priori knowledge is available, we can still achieve a better control of noise reduction and speech distortion by properly manipulating the Wiener filter, resulting in a suboptimal Wiener filter. In case that we have multiple microphone sensors, the multiple observations of the speech signal can be used to reduce noise with less or even no speech distortion

Despeckling of medical ultrasound images

Abstract: Speckle noise is an inherent property of medical ultrasound imaging, and it generally tends to reduce the image resolution and contrast, thereby reducing the diagnostic value of this imaging modality. As a result, speckle noise reduction is an important prerequisite, whenever ultrasound imaging is used for tissue characterization. Among the many methods that have been proposed to perform this task, there exists a class of approaches that use a multiplicative model of speckled image formation and take advantage of the logarithmical transformation in order to convert multiplicative speckle noise into additive noise. The common assumption made in a dominant number of such studies is that the samples of the additive noise are mutually uncorrelated and obey a Gaussian distribution. The present study shows conceptually and experimentally that this assumption is oversimplified and unnatural. Moreover, it may lead to inadequate performance of the speckle reduction methods. The study introduces a simple preprocessing procedure, which modifies the acquired radio-frequency images (without affecting the anatomical information they contain), so that the noise in the log-transformation domain becomes very close in its behavior to a white Gaussian noise. As a result, the preprocessing allows filtering methods based on assuming the noise to be white and Gaussian, to perform in nearly optimal conditions. The study evaluates performances of three different, nonlinear filters - wavelet denoising, total variation filtering, and anisotropic diffusion - and demonstrates that, in all these cases, the proposed preprocessing significantly improves the quality of resultant images. Our numerical tests include a series of computer-simulated and in vivo experiments.

Noise Estimation from a Single Image

Abstract: In order to work well, many computer vision algorithms require that their parameters be adjusted according to the image noise level, making it an important quantity to estimate. We show how to estimate an upper bound on the noise level from a single image based on a piecewise smooth image prior model and measured CCD camera response functions. We also learn the space of noise level functions how noise level changes with respect to brightness and use Bayesian MAP inference to infer the noise level function from a single image. We illustrate the utility of this noise estimation for two algorithms: edge detection and featurepreserving smoothing through bilateral filtering. For a variety of different noise levels, we obtain good results for both these algorithms with no user-specified inputs.

A mathematical model of noise in narrowband power line communication systems

Abstract: This manuscript introduces a mathematically tractable and accurate model of narrowband power line noise based on experimental measurements. In this paper, the noise is expressed as a Gaussian process whose instantaneous variance is a periodic time function. With this assumption and representation, the cyclostationary features of power line noise can be described in close form. The periodic function that represents the variance is then approximated with a small number of parameters. The noise waveform generated with this model shows good agreement with that of actually measured noise. Noise waveforms generated by different models are also compared with that of the proposed model.

TOPS: new DOA estimator for wideband signals

Abstract: This paper introduces a new direction-of-arrival (DOA) estimation algorithm for wideband sources called test of orthogonality of projected subspaces (TOPS). This new technique estimates DOAs by measuring the orthogonal relation between the signal and the noise subspaces of multiple frequency components of the sources. TOPS can be used with arbitrary shaped one-dimensional (1-D) or two-dimensional (2-D) arrays. Unlike other coherent wideband methods, such as the coherent signal subspace method (CSSM) and WAVES, the new method does not require any preprocessing for initial values. The performance of those wideband techniques and incoherent MUSIC is compared with that of the new method through computer simulations. The simulations show that this new technique performs better than others in mid signal-to-noise ratio (SNR) ranges, while coherent methods work best in low SNR and incoherent methods work best in high SNR. Thus, TOPS fills a gap between coherent and incoherent methods.

A fuzzy impulse noise detection and reduction method

Abstract: Removing or reducing impulse noise is a very active research area in image processing. In this paper we describe a new algorithm that is especially developed for reducing all kinds of impulse noise: fuzzy impulse noise detection and reduction method (FIDRM). It can also be applied to images having a mixture of impulse noise and other types of noise. The result is an image quasi without (or with very little) impulse noise so that other filters can be used afterwards. This nonlinear filtering technique contains two separated steps: an impulse noise detection step and a reduction step that preserves edge sharpness. Based on the concept of fuzzy gradient values, our detection method constructs a fuzzy set impulse noise. This fuzzy set is represented by a membership function that will be used by the filtering method, which is a fuzzy averaging of neighboring pixels. Experimental results show that FIDRM provides a significant improvement on other existing filters. FIDRM is not only very fast, but also very effective for reducing little as well as very high impulse noise.

Sparse Signal Detection from Incoherent Projections

Abstract: The recently introduced theory of compressed sensing (CS) enables the reconstruction or approximation of sparse or compressible signals from a small set of incoherent projections; often the number of projections can be much smaller than the number of Nyquist rate samples. In this paper, we show that the CS framework is information scalable to a wide range of statistical inference tasks. In particular, we demonstrate how CS principles can solve signal detection problems given incoherent measurements without ever reconstructing the signals involved. We specifically study the case of signal detection in strong inference and noise and propose an incoherent detection and estimation algorithm (IDEA) based on matching pursuit. The number of measurements and computations necessary for successful detection using IDEA is significantly lower than that necessary for successful reconstruction. Simulations show that IDEA is very resilient to strong interference, additive noise, and measurement quantization. When combined with random measurements, IDEA is applicable to a wide range of different signal classes

Codebook driven short-term predictor parameter estimation for speech enhancement

Abstract: In this paper, we present a new technique for the estimation of short-term linear predictive parameters of speech and noise from noisy data and their subsequent use in waveform enhancement schemes. The method exploits a priori information about speech and noise spectral shapes stored in trained codebooks, parameterized as linear predictive coefficients. The method also uses information about noise statistics estimated from the noisy observation. Maximum-likelihood estimates of the speech and noise short-term predictor parameters are obtained by searching for the combination of codebook entries that optimizes the likelihood. The estimation involves the computation of the excitation variances of the speech and noise auto-regressive models on a frame-by-frame basis, using the a priori information and the noisy observation. The high computational complexity resulting from a full search of the joint speech and noise codebooks is avoided through an iterative optimization procedure. We introduce a classified noise codebook scheme that uses different noise codebooks for different noise types. Experimental results show that the use of a priori information and the calculation of the instantaneous speech and noise excitation variances on a frame-by-frame basis result in good performance in both stationary and nonstationary noise conditions.

State/noise estimator for descriptor systems with application to sensor fault diagnosis

Abstract: For descriptor systems with measurement output noises (input disturbances may exist at the same time), a new descriptor estimator technique is developed. The necessary and sufficient condition for the existence of the present estimator is derived, and a systematic design approach is addressed. The effect of uncertainties is decoupled completely, and the asymptotic estimates of the descriptor system state and the output noise are obtained simultaneously. Furthermore, a normal state/disturbance estimator is also given. For a class of nonlinear descriptor systems with both output noises and input uncertainties, a nonlinear descriptor estimator is derived by using the proposed design approach, together with the linear matrix inequality technique. The existence and convergence of the nonlinear descriptor estimator is proven. The asymptotic estimates of the descriptor nonlinear system state and the output noise are obtained at the same time. The present estimators are applied to the sensor fault diagnosis, and hence the sensor fault can be estimated asymptotically. Finally, two numerical examples are included to illustrate the proposed design procedures and applications.

An unbiased FIR filter for TIE model of a local clock in applications to GPS-based timekeeping

Abstract: An unbiased finite impulse response (FIR) filter is proposed to estimate the time-interval error (TIE) K-degree polynomial model of a local clock in Global Positioning System (GPS)-based timekeeping in the presence of noise that is not obligatory Gaussian. Generic coefficients for the unbiased FIRs are derived. The low-degree FIRs and noise power gains are given. An estimation algorithm is proposed and examined for the TIE measurements of a crystal clock in the presence of the uniformly distributed sawtooth noise induced by the multichannel GPS timing receiver. Based upon this algorithm, we show that the unbiased FIR estimates are consistent with the reference (rubidium) measurements and fit them better than the standard Kalman filter

Oscillatory stability limit prediction using stochastic subspace identification

Abstract: Determining stability limits and maximum loading margins in a power system is important and can be of significant help for system operators for preventing stability problems In this paper, stochastic subspace identification is employed to extract the critical mode(s) from the measured ambient noise without requiring artificial disturbances (eg, line outages, generator tripping, and adding/removing loads), so that the identified critical mode may be used as an online index to predict the closest oscillatory instability The proposed index is not only independent of system models and truly represents the actual system, but it is also computationally efficient The application of the proposed index to several realistic test systems is examined using a transient stability program and PSCAD/EMTDC, which has detailed models that can capture the full dynamic response of the system The results show the feasibility of using the proposed identification technique and index for online detection of proximity to oscillatory stability problems

Evaluation of traffic noise pollution in Amman, Jordan.

Abstract: The City of Amman, Jordan, has been subjected to persistent increase in road traffic due to overall increase in prosperity, fast development and expansion of economy, travel and tourism. This study investigates traffic noise pollution in Amman. Road traffic noise index L10(1 h) was measured at 28 locations that cover most of the City of Amman. Noise measurements were carried out at these 28 locations two times a day for a period of one hour during the early morning and early evening rush hours, in the presence and absence of a barrier. The Calculation of Road Traffic Noise (CRTN) prediction model was employed to predict noise levels at the locations chosen for the study. Data required for the model include traffic volume, speed, percentage of heavy vehicles, road surface, gradient, obstructions, distance, noise path, intervening ground, effect of shielding, and angle of view. The results of the investigation showed that the minimum and the maximum noise levels are 46 dB(A) and 81 dB(A) during day-time and 58 dB(A) and 71 dB(A) during night-time. The measured noise level exceeded the 62 dB(A) acceptable limit at most of the locations. The CTRN prediction model was successful in predicting noise levels at most of the locations chosen for this investigation, with more accurate predictions for night-time measurements.

Systems and Methods for Providing Noise Leveling in a Breathing Assistance System

Abstract: A breathing assistance system having active noise control may include a gas delivery system, a patient interface, a connection system, and a noise control system. The gas delivery system may supply breathing gas to a patient via the connection system and the patient interface. The noise control system may include a processor, a speaker, a reference signal source, and a feedback sensor. The processor may generate noise control signals to be output by the speaker for canceling noise caused by a noise source of the breathing assistance system. The reference signal source may communicate reference signals associated with the noise source. The feedback sensor may detect noise caused by the noise source and noise control signals output by the speaker, and communicate to the processor feedback noise signals based on the detected noise. The processor may generate the noise control signals based at least on the reference signals and the feedback noise signals.

Coherent network analysis technique for discriminating gravitational-wave bursts from instrumental noise

Abstract: The sensitivity of current searches for gravitational-wave bursts is limited by non-Gaussian, nonstationary noise transients which are common in real detectors. Existing techniques for detecting gravitational-wave bursts assume the output of the detector network to be the sum of a stationary Gaussian noise process and a gravitational-wave signal. These techniques often fail in the presence of noise nonstationarities by incorrectly identifying such transients as possible gravitational-wave bursts. Furthermore, consistency tests currently used to try to eliminate these noise transients are not applicable to general networks of detectors with different orientations and noise spectra. In order to address this problem we introduce a fully coherent consistency test that is robust against noise nonstationarities and allows one to distinguish between gravitational-wave bursts and noise transients in general detector networks. This technique does not require any a priori knowledge of the putative burst waveform.

Estimating random errors due to shot noise in backscatter lidar observations

Abstract: We discuss the estimation of random errors due to shot noise in backscatter lidar observations that use either photomultiplier tube (PMT) or avalanche photodiode (APD) detectors. The statistical characteristics of photodetection are reviewed, and photon count distributions of solar background signals and laser backscatter signals are examined using airborne lidar observations at 532 nm using a photon-counting mode APD. Both distributions appear to be Poisson, indicating that the arrival at the photodetector of photons for these signals is a Poisson stochastic process. For Poisson- distributed signals, a proportional, one-to-one relationship is known to exist between the mean of a distribution and its variance. Although the multiplied photocurrent no longer follows a strict Poisson distribution in analog-mode APD and PMT detectors, the proportionality still exists between the mean and the variance of the multiplied photocurrent. We make use of this relationship by introducing the noise scale factor (NSF), which quantifies the constant of proportionality that exists between the root mean square of the random noise in a measurement and the square root of the mean signal. Using the NSF to estimate random errors in lidar measurements due to shot noise provides a significant advantage over the conventional error estimation techniques, in that with the NSF, uncertainties can be reliably calculated from or for a single data sample. Methods for evaluating the NSF are presented. Algorithms to compute the NSF are developed for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations lidar and tested using data from the Lidar In-space Technology Experiment.

Substrate Noise Coupling in SoC Design: Modeling, Avoidance, and Validation

Abstract: Issues related to substrate noise in system-on-chip design are described including the physical phenomena responsible for its creation, coupling transmission mechanisms and media, parameters affecting coupling strength, and its impact on mixed-signal integrated circuits. Design guidelines and best practices to minimize the generation, transmission, and reception of substrate noise are outlined, and different modeling approaches and computer simulation methods used in quantifying the noise coupling phenomena are presented. Finally, experiments that validate the modeling approaches and mitigation techniques are reviewed

High-Frequency Noise of Modern MOSFETs: Compact Modeling and Measurement Issues

Abstract: Compact modeling of the most important high-frequency (HF) noise sources of the MOSFET is presented in this paper, along with challenges in noise measurement and deembedding of future CMOS technologies. Several channel thermal noise models are reviewed and their ability to predict the channel noise of extremely small devices is discussed. The impact of technology scaling on noise performance of MOSFETs is also investigated by means of analytical expressions. It is shown that the gate tunneling current has a significant impact on MOSFETs noise parameters, especially at lower frequencies. Limitations of some commonly used noise models in predicting the HF noise parameters of modern MOSFETs are addressed and methods to alleviate some of the limitations are discussed

Emotion Recognition in the Noise Applying Large Acoustic Feature Sets

Abstract: Speech emotion recognition is considered mostly under ideal acoustic conditions: acted and elicited samples in studio quality are used besides sparse works on spontaneous fielddata. However, specific analysis of noise influence plays an important factor in speech processing and is practically not considered hereon, yet. We therefore discuss affect estimation under noise conditions herein. On 3 well-known public databases - DES, EMO-DB, and SUSAS - effects of postrecording noise addition in diverse dB levels, and performance under noise conditions during signal capturing, are shown. To cope with this new challenge we extend generation of functionals by extraction of a large 4k hi-level feature set out of more than 60 partially novel base contours. Such comprise among others intonation, intensity, formants, HNR, MFCC, and VOC19. Fast Information-Gain-Ratio filter-selection picks attributes according to noise conditions. Results are presented using Support Vector Machines as classifier.

The Influence of Operating Systems on the Performance of Collective Operations at Extreme Scale

Abstract: We investigate operating system noise, which we identify as one of the main reasons for a lack of synchronicity in parallel applications. Using a microbenchmark, we measure the noise on several contemporary platforms and find that, even with a general - purpose operating system, noise can be limited if certain precautions are taken. We then inject artificially generated noise into a massively parallel system and measure its influence on the performance of collective operations. Our experiments indicate that on extreme - scale platforms, the performance is correlated with the largest interruption to the application, even if the probability of such an interruption is extremely small. We demonstrate that synchronizing the noise can significantly reduce its negative influence.

Estimation of Nonlinear Errors-in-Variables Models for Computer Vision Applications

Abstract: In an errors-in-variables (EIV) model, all the measurements are corrupted by noise. The class of EIV models with constraints separable into the product of two nonlinear functions, one solely in the variables and one solely in the parameters, is general enough to represent most computer vision problems. We show that the estimation of such nonlinear EIV models can be reduced to iteratively estimating a linear model having point dependent, i.e., heteroscedastic, noise process. Particular cases of the proposed heteroscedastic errors-in-variables (HEIV) estimator are related to other techniques described in the vision literature: the Sampson method, renormalization, and the fundamental numerical scheme. In a wide variety of tasks, the HEIV estimator exhibits the same, or superior, performance as these techniques and has a weaker dependence on the quality of the initial solution than the Levenberg-Marquardt method, the standard approach toward estimating nonlinear models

Local noise weighted filtering for emphysema scoring of low-dose CT images

Abstract: Computed tomography (CT) has become the new reference standard for quantification of emphysema. The most popular measure of emphysema derived from CT is the pixel index (PI), which expresses the fraction of the lung volume with abnormally low intensity values. As PI is calculated from a single, fixed threshold on intensity, this measure is strongly influenced by noise. This effect shows up clearly when comparing the PI score of a high-dose scan to the PI score of a low-dose (i.e., noisy) scan of the same subject. In this paper, the noise variance (NOVA) filter is presented: a general framework for (iterative) nonlinear filtering, which uses an estimate of the spatially dependent noise variance in an image. The NOVA filter iteratively estimates the local image noise and filters the image. For the specific purpose of emphysema quantification of low-dose CT images, a dedicated, noniterative NOVA filter is constructed by using prior knowledge of the data to obtain a good estimate of the spatially dependent noise in an image. The performance of the NOVA filter is assessed by comparing characteristics of pairs of high-dose and low-dose scans. The compared characteristics are the PI scores for different thresholds and the size distributions of emphysema bullae. After filtering, the PI scores of high-dose and low-dose images agree to within 2%-3%points. The reproducibility of the high-dose bullae size distribution is also strongly improved. NOVA filtering of a CT image of typically 400/spl times/512/spl times/512 voxels takes only a couple of minutes which makes it suitable for routine use in clinical practice.

Hearing aid with suppression of wind noise

Abstract: The present application relates to a hearing aid with suppression of wind noise wherein wind noise detection is provided involving only a single comparison of the input signal power level at first low frequencies with the input signal power level at frequencies that may include the first low frequencies whereby a computational cost effective and simple wind noise detection is provided The determination of relative power levels of the input signal reflects the shape of the power spectrum of the signal, and the detection scheme is therefore typically capable of distinguishing music from wind noise so that attenuation of desired music is substantially avoided

Recovery of spectral reflectances of objects being imaged without prior knowledge

Abstract: Prior knowledge of the noise present in a color image acquisition device is very important in estimating colorimetric values or in recovering the spectral reflectances of pixels of objects being imaged, since these values are greatly influenced by the noise. In this paper, a new model is proposed for the determination of the noise variance of a multispectral color image acquisition system and experimental results to demonstrate its accuracy are presented. It is demonstrated that the noise variance of an actual multispectral color image acquisition system computed by the proposal agrees fairly well with the variance which minimizes the mean-square error of the recovered reflectances by the Wiener filter. As an application of the proposal, it is shown that spectral reflectances of an art painting are recovered accurately by the use of sensor responses without prior knowledge of objects being imaged and noise present in an image acquisition system.

Noise suppression with high speech quality based on weighted noise estimation and MMSE STSA

Abstract: This paper proposes a high speech quality noise suppression method based on weighted noise estimation and MMSE STSA. The proposed method continuously updates the noise estimate, using weighted noisy speech according to the estimated speech-to-noise ratio. In order to fully utilize the improvement offered by noise estimation, the spectral gain is corrected according to the estimated speech-to-noise ratio. By using accurate noise estimation, more accurate SNR than in the conventional method is obtained, which helps to reduce distortion in the enhanced speech. In subjective speech quality evaluations, the five-stage MOS was improved by 0.35 and 0.40 at the maximum, respectively, for the cases in which the speech was encoded and was not encoded after noise suppression. The improved version, which was developed on the basis of the proposed noise suppressor, satisfies all 3GPP minimum requirements for speech quality and has been installed in a commercially available model. © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 89(2): 43–53, 2006; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/ecjc.20145

The influence of amplifier, interface and biological noise on signal quality in high-resolution EEG recordings

Abstract: First, the intrinsic random noise sources of a biopotential measurement in general are reviewed. For the special case of an electroencephalographic (EEG) measurement we have extended the commonly used amplifier noise model by biological generated background noise. As the strongest of all noise sources involved will dominate the resulting signal to noise ratio (S/N), we have investigated under which conditions this will be the case. We illustrate experimentally that up to 100 Hz S/N practically depends only on cortical generated background noise, while at a few hundred Hz or more amplifier and thermal noise of interelectrode resistance are the major sources.

Spread Spectrum Audio Steganography Using Sub-band Phase Shifting

Abstract: This paper presents an improvement of spread spectrum audio data hiding methods. We introduce phase shifting in audio signals to reduce the correlation with PN signal per each sub-band. It allows easy detection of the embedded data signal from audio when de-spreading the compound signal. The paper reports the subjective test results and the measurements of noise resiliency. The proposed method generates the quality degradation at the same level of NMR +3dB, but accests +6dB noise, therefore, the method has 3dB benefits.