Adding noise to inputs of some suboptimal detectors or estimators can improve their performance under certain conditions. In the literature, noise benefits have been studied for detection and estimation systems separately. In this study, noise benefits are investigated for joint detection and estimation systems. The analysis is performed under the Neyman–Pearson (NP) and Bayesian detection fram...

The performance of many noise-robust automatic speech recognition (ASR) methods, such as vector Taylor series (VTS) feature compensation, heavily depends on an estimation of the noise that contaminates speech. Therefore, providing accurate noise estimates for this kind of methods is crucial as well as a challenge. In this paper we investigate the use of deep neural networks (DNNs) to perform no...

This paper addresses the problem of noise estimation in the context of front-end speech enhancement for automatic speech recognition. A recently proposed approach uses harmonic analysis of degraded speech to detect regions in the frequency spectrum where reliable noise estimates may be sought. In this paper an analogous LPC-derived spectrum is used to locate low energy regions between resonant ...

This paper presents a two-microphone speech enhancer designed to remove noise in hands-free car kits. The algorithm, based on the magnitude squared coherence, uses speech correlation and noise decorrelation to separate speech from noise. The remaining correlated noise is reduced using cross-spectral subtraction. Particular attention is focused on the estimation of the different spectral densiti...

The objective of this paper is to do the estimation of the noise in the Magnetic Resonance images and evaluate the noise reduction algorithm present in this paper. We propose a method for reduction of Rician noise in MRI. This method shows an optimal estimation result that is more accurate in recovering the true signal from Rician noise. The method proposed specifically for Rician noise reducti...

We present an algorithm for recursive estimation of parameters in a mildly nonlinear model involving incomplete data. In particular, we focus on the time-varying deterministic parameters of additive noise in the nonlinear model. For the nonstationary noise that we encounter in robust speech recognition, different observation data segments correspond to different noise parameter values. Hence, r...

We present iterative channel estimation and decoding schemes for multi-input multi-output (MIMO) Rayleigh block fading channels in spatially correlated noise. An expectation-maximization (EM) algorithm is utilized to find the maximum likelihood (ML) estimates of the channel and spatial noise covariance matrices, and to compute soft information of coded symbols which is sent to an error-control ...

Alpha stable noise, determined by four parameters, has been found in the random error of a laser gyroscope. Accurate estimation of the four parameters is the key process for analyzing the properties of alpha stable noise. Three widely used estimation methods-quantile, empirical characteristic function (ECF) and logarithmic moment method-are analyzed in contrast with Monte Carlo simulation in th...

MAXIMUM LIKELIHOOD ESTIMATION OF EXPONENTIALS CONTAINED IN SIGNAL-DEPENDENT NOISES Publication No._______ Steven John Apollo, Ph.D. The University of Texas at Arlington, 1991 Supervising Professor: Michael T. Manry The problem of maximum likelihood estimation (MLE) of exponentials in signal-dependent noise is addressed as well as a methodology to attack the problem. Estimation of exponentials h...

Blind estimation of the signal-to-noise ratio in noisy speech recordings is useful to enhance the performance of many speech processing algorithms. Most current techniques are efficient in low noise environments only, justifying the need for a high noise estimator, such as the one presented here. A pitch tracker robust in high noise was developed and is used to create a two-dimensional represen...