White noise

About: White noise is a research topic. Over the lifetime, 16496 publications have been published within this topic receiving 318633 citations.

Filtering of colored noise for speech enhancement and coding

Abstract: Scalar and vector Kalman filters are implemented for filtering speech contaminated by additive white noise or colored noise, and an iterative signal and parameter estimator which can be used for both noise types is presented. Particular emphasis is placed on the removal of colored noise, such as helicopter noise, by using state-of-the-art colored-noise-assumption Kalman filters. The results indicate that the colored noise Kalman filters provide a significant gain in signal-to-noise ratio (SNR), a visible improvement in the sound spectrogram, and an audible improvement in output speech quality, none of which are available with white-noise-assumption Kalman and Wiener filters. When the filter is used as a prefilter for linear predictive coding, the coded output speech quality and intelligibility are enhanced in comparison to direct coding of the noisy speech. >

Persistence of Dynamical Systems under Random Perturbations

Abstract: Random perturbations may decisively affect the long-term behavior of dynamical systems. Random effects are modeled by the addition of Gaussian white noise to the system. The resulting diffusion equ...

Subspace model identification Part 2. Analysis of the elementary output-error state-space model identification algorithm

Abstract: The elementary MOESP algorithm presented in the first part of this series of papers is analysed in this paper. This is done in three different ways. First, we study the asymptotic properties of the estimated state-space model when only considering zero-mean white noise perturbations on the output sequence. It is shown that, in this case, the MOESPl implementation yields asymptotically unbiased estimates. An important constraint to this result is that the underlying system must have a finite impulse response and subsequently the size of the Hankel matrices, constructed from the input and output data at the beginning of the computations, depends on the number of non-zero Markov parameters. This analysis, however, leads to a second implementation of the elementary MOESP scheme, namely MOESP2. The latter implementation has the same asymptotic properties without the finite impulse response constraint. Secondly, we compare the MOESP2 algorithm with a classical state space model identification scheme. The latter...

Resonances arising from hydrodynamic memory in Brownian motion

Abstract: In Brownian motion, a particle's movement is driven by rapid collisions with the surrounding solvent molecules; this thermal force is assumed to be random and characterized by a Gaussian white noise spectrum. Friction between the particle and the viscous solvent damps its motion. However, the displaced fluid acts back on the particle, giving rise to a hydrodynamic 'memory' and thermal forces with a coloured noise spectrum. Direct experimental observation of a coloured spectrum has proved difficult. Sylvia Jeney and colleagues now report clear evidence for it in measurements of the Brownian fluctuations of a microsphere in a strong optical trap. They anticipate that such details in thermal noise could be exploited for the development of new types of sensors and particle-based assays in lab-on-a-chip applications. Observation of the Brownian motion of a small probe interacting with its environment provides one of the main strategies for characterizing soft matter1,2,3,4. Essentially, two counteracting forces govern the motion of the Brownian particle. First, the particle is driven by rapid collisions with the surrounding solvent molecules, referred to as thermal noise. Second, the friction between the particle and the viscous solvent damps its motion. Conventionally, the thermal force is assumed to be random and characterized by a Gaussian white noise spectrum. The friction is assumed to be given by the Stokes drag, suggesting that motion is overdamped at long times in particle tracking experiments, when inertia becomes negligible. However, as the particle receives momentum from the fluctuating fluid molecules, it also displaces the fluid in its immediate vicinity. The entrained fluid acts back on the particle and gives rise to long-range correlations5,6. This hydrodynamic ‘memory’ translates to thermal forces, which have a coloured, that is, non-white, noise spectrum. One hundred years after Perrin’s pioneering experiments on Brownian motion7,8,9, direct experimental observation of this colour is still elusive10. Here we measure the spectrum of thermal noise by confining the Brownian fluctuations of a microsphere in a strong optical trap. We show that hydrodynamic correlations result in a resonant peak in the power spectral density of the sphere’s positional fluctuations, in strong contrast to overdamped systems. Furthermore, we demonstrate different strategies to achieve peak amplification. By analogy with microcantilever-based sensors11,12, our results reveal that the particle–fluid–trap system can be considered a nanomechanical resonator in which the intrinsic hydrodynamic backflow enhances resonance. Therefore, instead of being treated as a disturbance, details in thermal noise could be exploited for the development of new types of sensor and particle-based assay in lab-on-a-chip applications13,14.