Noise measurement

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

Characterizing and quantifying noise in PMU data

Abstract: Data recorded by Phasor Measurement Units (PMUs) contains noise. This paper characterizes and quantifies this noise for voltage, current and frequency data recorded at three different voltage levels. The probability distribution of the measurement noise and its typical power are identified. The PMU noise quantification can help in generation of experimental PMU data in close conformity with field PMU data, bad data removal, missing data prediction, and effective design of statistical filters for noise rejection.

Classification image weights and internal noise level estimation.

Abstract: For the linear discrimination of two stimuli in white Gaussian noise in the presence of internal noise, a method is described for estimating linear classification weights from the sum of noise images segregated by stimulus and response. The recommended method for combining the two response images for the same stimulus is to difference the average images. Weights are derived for combining images over stimuli and observers. Methods for estimating the level of internal noise are described with emphasis on the case of repeated presentations of the same noise sample. Simple tests for particular hypotheses about the weights are shown based on observer agreement with a noiseless version of the hypothesis.

Eigenstructure methods for direction of arrival estimation in the presence of unknown noise fields

Abstract: In eigenstructure methods for direction of arrival estimation of signal wavefronts, the additive noise is assumed to be spatially white, i.e., of equal power and uncorrelated from sensor to sensor. When the noise is nonwhite but has a known covariance, we can still handle the problem through prewhitening. However, there are no techniques presently available to deal with completely unknown noise fields. In this paper, we propose a solution to this unknown noise covariance problem for the case when the noise field is invariant under two measurements of the array covariance; situations where this assumption is valid are not uncommon in sonar applications. In fact, the idea has been used in certain so-called "despoking" algorithms for conventional beamformers. Results of computer simulations carried out to compare the performance of the new algorithm to earlier methods are also presented.

Speech Intelligibility Potential of General and Specialized Deep Neural Network Based Speech Enhancement Systems

Abstract: In this paper, we study aspects of single microphone speech enhancement (SE) based on deep neural networks (DNNs). Specifically, we explore the generalizability capabilities of state-of-the-art DNN-based SE systems with respect to the background noise type, the gender of the target speaker, and the signal-to-noise ratio (SNR). Furthermore, we investigate how specialized DNN-based SE systems, which have been trained to be either noise type specific, speaker specific or SNR specific, perform relative to DNN based SE systems that have been trained to be noise type general, speaker general, and SNR general. Finally, we compare how a DNN-based SE system trained to be noise type general, speaker general, and SNR general performs relative to a state-of-the-art short-time spectral amplitude minimum mean square error (STSA-MMSE) based SE algorithm. We show that DNN-based SE systems, when trained specifically to handle certain speakers, noise types and SNRs, are capable of achieving large improvements in estimated speech quality (SQ) and speech intelligibility (SI), when tested in matched conditions. Furthermore, we show that improvements in estimated SQ and SI can be achieved by a DNN-based SE system when exposed to unseen speakers, genders and noise types, given a large number of speakers and noise types have been used in the training of the system. In addition, we show that a DNN-based SE system that has been trained using a large number of speakers and a wide range of noise types outperforms a state-of-the-art STSA-MMSE based SE method, when tested using a range of unseen speakers and noise types. Finally, a listening test using several DNN-based SE systems tested in unseen speaker conditions show that these systems can improve SI for some SNR and noise type configurations but degrade SI for others.

Nonlinear mean filters in image processing

Abstract: The use of nonlinear means in image processing is introduced. The properties of these means in the presence of different types of noise are investigated. It is shown that nonlinear filters based on these means behave well for both additive and impulse noise. Their performance in the presence of signal dependent noise is satisfactory. They preserve the edges better than linear filters, and they reject the noise better than median filters.