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 published on a yearly basis
Papers
More filters
TL;DR: A new filter is proposed that allows for the consistent treatment of a class of control problem involving nonlinear estimation from measurements with state-dependent noise and is computed by an iterative root-searching method that maximizes a maximum likelihood function.
Abstract: We consider the problem of estimating the state of a system when measurement noise is a function of the system's state. We propose generalizations of the extended Kalman filter and the iterated extended Kalman filter that can be utilized when the state estimate distribution is approximately Gaussian. The state estimate is computed by an iterative root-searching method that maximizes a maximum likelihood function. The new filter allows for the consistent treatment of a class of control problem involving nonlinear estimation from measurements with state-dependent noise. The effectiveness of the estimation algorithm is illustrated for a control problem with a mobile bearing-only sensor.
62 citations
Patent•
10 Aug 1994TL;DR: In this paper, the authors proposed a method to reduce the probability of coding low energy unvoiced speech as background noise by examining subbands of the input signal, which can be distinguished from background noise.
Abstract: It is a first objective of the present invention to provide a method by which to reduce the probability of coding low energy unvoiced speech as background noise. The present invention determines an encoding rate by examining subbands of the input signal, by this method unvoiced speech can be distinguished from background noise. A second objective of the present invention is to provide a means by which to set the threshold levels that takes into account signal energy as well as background noise energy. In the present invention, the background noise is not used to determine threshold values, rather the signal to noise ratio of an input signal is use to determine the threshold values. A third objective of the present invention is to provide a method for coding music passing through a variable rate vocoder. The present invention examines the periodicity of the input signal to distinguish music from background noise.
61 citations
TL;DR: In this paper, the authors measured the FM and AM noise spectra of 1.3 μm InGaAsP DFB lasers in the frequency range from dc to 4 GHz and compared the relaxation resonances appearing in these spectra to the semiclassical theory of laser noise.
Abstract: The FM and AM noise spectra of 1.3 μm InGaAsP DFB lasers are measured in the frequency range from dc to 4 GHz. The relaxation resonances appearing in these spectra are compared to the semiclassical theory of laser noise. All the resonance parameters, i.e., the linewidth enhancement factor α, the resonance frequency f R , and the damping constant γ e , are determined from the FM noise spectra by successful curve fitting. The estimated value of α is 2.2. Field spectra for various bias currents are measured by using optical heterodyne detection. Theoretical lineshapes are obtained by using four noise-parameter values which have been determined from the FM noise and the linewidth measurements. The results are in excellent agreement with the measured spectra. This agreement verifies the estimation that \alpha = 2.2.
61 citations
TL;DR: This work generalizes the variational Bayesian approximation based adaptive Kalman filter (VB_AKF) from the single sensor filtering to a multi-sensor fusion system, and proposes two new centralized fusion algorithms to deal with the case that the measurement noise variance is unknown.
Abstract: The work presented here solves the multi-sensor centralized fusion problem in the linear Gaussian model without the measurement noise variance. We generalize the variational Bayesian approximation based adaptive Kalman filter (VB_AKF) from the single sensor filtering to a multi-sensor fusion system, and propose two new centralized fusion algorithms, i.e., VB_AKF-based augmented centralized fusion algorithm and VB_AKF-based sequential centralized fusion algorithm, to deal with the case that the measurement noise variance is unknown. The simulation results show the effectiveness of the proposed algorithms.
61 citations
16 Sep 2016
TL;DR: It is shown in simulation experiments that a male-speaker and text-independent DRNN based SE front-end, without specific a priori knowledge about the noise type outperforms a text, noise type and speaker dependent NMF basedFront-end as well as a STSA-MMSE based front- end in terms of Equal Error Rates for a large range of noise types and signal to noise ratios on the RSR2015 speech corpus.
Abstract: In this paper we propose to use a state-of-the-art Deep Recurrent Neural Network (DRNN) based Speech Enhancement (SE) algorithm for noise robust Speaker Verification (SV). Specifically, we study the performance of an i-vector based SV system, when tested in noisy conditions using a DRNN based SE front-end utilizing a Long Short-Term Memory (LSTM) architecture. We make comparisons to systems using a Non-negative Matrix Factorization (NMF) based front-end, and a Short-Time Spectral Amplitude Minimum Mean Square Error (STSA-MMSE) based front-end, respectively. We show in simulation experiments that a male-speaker and text-independent DRNN based SE front-end, without specific a priori knowledge about the noise type outperforms a text, noise type and speaker dependent NMF based front-end as well as a STSA-MMSE based front-end in terms of Equal Error Rates for a large range of noise types and signal to noise ratios on the RSR2015 speech corpus.
61 citations