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Noise measurement
About: Noise measurement is a research topic. Over the lifetime, 19776 publications have been published within this topic receiving 308180 citations.
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29 Apr 2002
TL;DR: Signals Characteristics at the Output of Linear System of the Generalized Detector under the Stimulus of Multiplicative Noise Signal Characteristics of Signals at the Generalization Detector Output under under the Stochastic Distribution Law of the Signal Probability Distribution Density.
Abstract: PROBABILITY AND STATISTICS Probability: Basic Concepts Random Variables Stochastic Processes Correlation Function Spectral Density Statistical Characteristics Conclusions References CLASSICAL AND MODERN APPROACHES TO SIGNAL DETECTION THEORY Gaussian Approach Markov Approach Bayes' Decision-Making Rule Unbiased and Invariant Decision-Making Rules Mini-Max Decision-Making Rule Sequential Signal Detection Signal Detection in Non-Gaussian Noise Non-Parametric Signal Detection Conclusions References MAIN CHARACTERISTICS OF MULTIPLICATIVE NOISE Classification of the Noise and Interference Sources of the Multiplicative Noise Classification and Main Properties of Multiplicative Noise Correlation Function and Energy Spectrum of Multiplicative Noise Generalized Statistical Model of Multiplicative Noise Conclusions References STATISTICAL CHARACTERISTICS OF SIGNALS UNDER THE STIMULUS OF MULTIPLICATIVE NOISE Deterministic and Quasideterministic Multiplicative Noise Stationary Fluctuating Multiplicative Noise Ensemble and Individual Realizations of the Signal Probability Distribution Density of the Signal in the Additive Gaussian Noise under the Stimulus of Multiplicative Noise Multivariate Probability Distribution Density of Instantaneous Values of the Signal under the Stimulus of Fluctuating Multiplicative Noise Conclusions References MAIN THEORETICAL PRINCIPLES OF THE GENERALIZED APPROACH TO SIGNAL PROCESSING UNDER THE STIMULUS OF MULTIPLICATIVE NOISE Basic Concepts Criticism Initial Premises Likelihood Ratio Engineering Interpretation Generalized Detector Distribution Law Conclusions References GENERALIZED APPROACH TO SIGNAL PROCESSING UNDER THE STIMULUS OF MULTIPLICATIVE NOISE AND LINEAR SYSTEMS Signal Characteristics at the Output of Linear System of the Generalized Detector under the Stimulus of Multiplicative Noise Signal Characteristics at the Generalized Detector Output under under the Stimulus of Multiplicative Noise Signal Noise Component for Some Types of Signals Signal Noise Component under the Stimulus of the Slow and Rapid Multiplicative Noise Signal Distribution Law under the Stimulus of Multiplicative Noise Conclusions References GENERALIZED APPROACH TO SIGNAL DETECTION IN THE PRESENCE OF MULTIPLICATIVE AND ADDITIVE GAUSSIAN NOISE Statistical Characteristics of Signals at the Output of the Generalized Detector Detection Performances of the Generalized Detector Known Correlation Function of the Multiplicative Noise One-Channel Generalized Detector Diversity Signal Detection Conclusions References SIGNAL PARAMETER MEASUREMENT PRECISION A Single Signal Parameter Measurement under a Combined Stimulus of Weak Multiplicative and Additive Gaussian Noise Simultaneous Measurement of Two Signal Parameters under a Combined Stimulus of Weak Multiplicative and Additive Gaussian Noise A Single Parameter Measurement under a Combined Stimulus of High Multiplicative and Additive Gaussian Noise Conclusions References SIGNAL RESOLUTION UNDER THE GENERALIZED APPROACH TO SIGNAL PROCESSING IN THE PRESENCE OF NOISE Estimation Criteria of Signal Resolution Signal Resolution by Woodward Criterion Statistical Criterion of Signal Resolution Conclusions References APPENDIX I: Delta Function APPENDIX II: Correlation Function and Energy Spectrum of Noise Modulation Function NOTATION INDEX INDEX
111 citations
TL;DR: Simulation results show that the proposed integration system with the adaptive filter is more effective in estimating the position and attitude errors than a system that uses the extended Kalman filter.
Abstract: The inertial navigation system (INS)/GPS integration system is designed by employing an adaptive filter that can estimate measurement noise variance using the residual of the measurement To verify the efficiency of the proposed loosely-coupled INS/GPS integration system, simulations were performed by assuming that GPS information has large position errors The simulation results show that the proposed integration system with the adaptive filter is more effective in estimating the position and attitude errors than a system that uses the extended Kalman filter
110 citations
01 Jan 2006
TL;DR: Generation of functionals is extended by extraction of a large 4k hi-level feature set out of more than 60 partially novel base contours that comprise among others intonation, intensity, formants, HNR, MFCC, and VOC19, and Fast Information-Gain-Ratio filter-selection picks attributes according to noise conditions.
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.
110 citations
27 Jun 1994
TL;DR: In this article, the second and fourth order moments of the observed noisy signal are used to estimate the SNR of the noisy signal, and shape factors of the signal's and the noise's probability density functions are used.
Abstract: An algorithm is presented that allows an estimation of the SNR just by the observation of the noisy signal. For the estimation, shape factors of the signal's and the noise's probability density functions are used. The algorithm is based on the second and fourth order moments of the observed noisy signal. >
110 citations
04 May 2003
TL;DR: In this paper, the phase noise performance of a 10 MHz MEMS-based micromechanical resonator oscillator has been measured using sustaining circuits with and without automatic-level control (ALC), and with differing mechanisms for ALC.
Abstract: Clear differences in the phase noise performance of a 10 MHz MEMS-based micromechanical resonator oscillator have been measured using sustaining circuits with and without automatic-level control (ALC), and with differing mechanisms for ALC. In particular, low output power oscillators referenced to high-Q clamped-clamped beam /spl mu/mechanical resonators exhibit an unexpected 1/f/sup 3/ phase noise component without ALC, a 1/f/sup 5/ phase noise component when an ALC circuit based on resonator dc-bias adjustment is used, and finally, removal of these components when an ALC circuit based on sustaining amplifier gain control is used, in which case the expected 1/f/sup 2/ phase noise component is all that remains. That ALC is able to remove the 1/f/sup 3/ phase noise seen in non-ALC'ed oscillators suggests that this noise component emanates primarily from nonlinearity in the voltage-to-force capacitive transducer, either through direct aliasing of amplifier 1/f noise, or through instabilities introduced by spring softening (i.e., Duffing) phenomena.
110 citations