Speech enhancement with noise estimation in bark domain;

During the noise estimation,the estimation of its spectrum is updated by tracking the speech-absent frames.

After capturing theneighborhoods of discontinuity in the image and acquiring their spatial features based on noise estimation,adaptive optimal.

The method can track eigenvalue minima on each eigenvector without any distinction between the speech activity and the speech pause,thus updating the noise estimate throughout the entire signal.

When the statistics of noise are changing or signal-noise-ratio(SNR)is low,the noise estimated value by voice activity detection is not exact.

By applying white noise estimation theory in Krein space,a sufficient and necessary condition on the existence of an H∞ fault estimator was derived,and a solution was obtained in terms of matrix Riccati equation.

Based on Kalman filtering and white noise estimation theory, reduced-order Wiener state estimator for a canonical form of descriptor discrete- time stochastic linear systems is proposed by applying modern time series analysis approach.

A fast adaptive method for noise spectrum estimation is proposed in this paper.

In this algorithm,a noise spectrum estimation approach by minima controlled recursive averaging is utilized firstly,thus no voice activity detection is needed here,secondly,the occurrence possibility of "musical noise" is reduced by making use of a recursively calculated over-subtraction factor based on sub-band signal-to-noise ratio.