scispace - formally typeset
Search or ask a question
Topic

Noise measurement

About: Noise measurement is a research topic. Over the lifetime, 19776 publications have been published within this topic receiving 308180 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: The Cramer-Rao bound on the variance of angle-of-arrival estimates for arbitrary additive, independent, identically distributed, symmetric, non-Gaussian noise is presented and improved over initial robust estimates and is valid for a wide SNR range.
Abstract: Many approaches have been studied for the array processing problem when the additive noise is modeled with a Gaussian distribution, but these schemes typically perform poorly when the noise is non-Gaussian and/or impulsive. This paper is concerned with maximum likelihood array processing in non-Gaussian noise. We present the Cramer-Rao bound on the variance of angle-of-arrival estimates for arbitrary additive, independent, identically distributed (iid), symmetric, non-Gaussian noise. Then, we focus on non-Gaussian noise modeling with a finite Gaussian mixture distribution, which is capable of representing a broad class of non-Gaussian distributions that include heavy tailed, impulsive cases arising in wireless communications and other applications. Based on the Gaussian mixture model, we develop an expectation-maximization (EM) algorithm for estimating the source locations, the signal waveforms, and the noise distribution parameters. The important problems of detecting the number of sources and obtaining initial parameter estimates for the iterative EM algorithm are discussed in detail. The initialization procedure by itself is an effective algorithm for array processing in impulsive noise. Novel features of the EM algorithm and the associated maximum likelihood formulation include a nonlinear beamformer that separates multiple source signals in non-Gaussian noise and a robust covariance matrix estimate that suppresses impulsive noise while also performing a model-based interpolation to restore the low-rank signal subspace. The EM approach yields improvement over initial robust estimates and is valid for a wide SNR range. The results are also robust to PDF model mismatch and work well with infinite variance cases such as the symmetric stable distributions. Simulations confirm the optimality of the EM estimation procedure in a variety of cases, including a multiuser communications scenario. We also compare with existing array processing algorithms for non-Gaussian noise.

190 citations

Journal ArticleDOI
TL;DR: This new algorithm identifies islands of reliability (essentially the portion of speech contained between the first and the last vowel) using time and frequency-based features and then applies a noise adaptive procedure to refine the boundaries.
Abstract: The authors address the problem of automatic word boundary detection in quiet and in the presence of noise. Attention has been given to automatic word boundary detection for both additive noise and noise-induced changes in the talker's speech production (Lombard reflex). After a comparison of several automatic word boundary detection algorithms in different noisy-Lombard conditions, they propose a new algorithm that is robust in the presence of noise. This new algorithm identifies islands of reliability (essentially the portion of speech contained between the first and the last vowel) using time and frequency-based features and then, after a noise classification, applies a noise adaptive procedure to refine the boundaries. It is shown that this new algorithm outperforms the commonly used algorithm developed by Lamel (1981) et al. and several other recently developed methods. They evaluated the average recognition error rate due to word boundary detection in an HMM-based recognition system across several signal-to-noise ratios and noise conditions. The recognition error rate decreased to about 20% compared to an average of approximately 50% obtained with a modified version of the Lamel et al. algorithm. >

190 citations

Journal ArticleDOI
TL;DR: In this article, simple expressions for MESFET and HEMT noise wave parameters based on a linear equivalent circuit are derived for correlation matrices and a measurement technique is presented and experimentally compared with the conventional method.
Abstract: The noise wave approach is applied to analysis, modeling, and measurement applications. Methods are presented for the calculation of component and network noise wave correlation matrices. Embedding calculations, relations to two-port figures-of-merit, and transformations to traditional representations are discussed. Simple expressions are derived for MESFET and HEMT noise wave parameters based on a linear equivalent circuit. A noise wave measurement technique is presented and experimentally compared with the conventional method. >

188 citations

Patent
12 Feb 2002
TL;DR: In this paper, two or more signal detectors (e.g., microphones) are used to detect respective signals having speech and noise components, with the magnitude of each component being dependent on various factors such as the distance between the speech source and the microphone.
Abstract: Techniques to suppress noise from a signal comprised of speech plus noise. In accordance with aspects of the invention, two or more signal detectors (e.g., microphones) are used to detect respective signals having speech and noise components, with the magnitude of each component being dependent on various factors such as the distance between the speech source and the microphone. Signal processing is then used to process the detected signals to generate the desired output signal having predominantly speech with a large portion of the noise removed. The techniques described herein may be advantageously used for both near-field and far-field applications, and may be implemented in various mobile communication devices such as cellular phones.

188 citations

Journal ArticleDOI
TL;DR: In this article, the authors used a first-order, finite-state, discrete-time Markov process to extract small, single channel ion currents from background noise, which can be used to detect signals that do not conform to a firstorder Markov model, but the method is less accurate when the background noise is not white.
Abstract: Techniques for extracting small, single channel ion currents from background noise are described and tested. It is assumed that single channel currents are generated by a first-order, finite-state, discrete-time, Markov process to which is added `white' background noise from the recording apparatus (electrode, amplifiers, etc.). Given the observations and the statistics of the background noise, the techniques described here yield a posteriori estimates of the most likely signal statistics, including the Markov model state transition probabilities, duration (open- and closed-time) probabilities, histograms, signal levels, and the most likely state sequence. Using variations of several algorithms previously developed for solving digital estimation problems, we have demonstrated that: (1) artificial, small, first-order, finite-state, Markov model signals embedded in simulated noise can be extracted with a high degree of accuracy, (2) processing can detect signals that do not conform to a first-order Markov model but the method is less accurate when the background noise is not white, and (3) the techniques can be used to extract from the baseline noise single channel currents in neuronal membranes. Some studies have been included to test the validity of assuming a first-order Markov model for biological signals. This method can be used to obtain directly from digitized data, channel characteristics such as amplitude distributions, transition matrices and open- and closed-time durations.

188 citations


Network Information
Related Topics (5)
Feature extraction
111.8K papers, 2.1M citations
88% related
Convolutional neural network
74.7K papers, 2M citations
84% related
Deep learning
79.8K papers, 2.1M citations
84% related
Artificial neural network
207K papers, 4.5M citations
83% related
Wireless
133.4K papers, 1.9M citations
83% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202377
2022162
2021495
2020525
2019489
2018755