This paper deals with the problem of speech enhancement when only a corrupted speech signal is available for processing. Kalman filtering is known as an effective speech enhancement technique, in which speech signal is usually modeled as autoregressive (AR) model and represented in the state-space domain. Various approaches based on the Kalman filter are presented in the literature. They usuall...

We investigate the convergence properties of the forgetting factor RLS algorithm in a stationary data environment. Using the settling time as our performance measure, we show that the algorithm exhibits a variable performance that depends on the particular combination of the initialization and noise level. Specifically when the observation noise level is low (high SNR) RLS, when initialized wit...

This paper discusses a method for estimating glottal flow derivative model parameters using the wavelet-smoothed excitation. The excitation is first estimated using the Weighted Recursive Least Squares with Variable Forgetting Factor algorithm. The raw excitation is then smoothed by applying a Discrete Wavelet Transform (DWT) using Biorthogonal Quadrature filters, and a thresholding operation d...

This paper studies the performance of the aposteriori recursive least squares lattice lter in the presence of a nonstationary chirp signal. The forward and backward partial correlation (PARCOR) coe cients for a Wiener-Hopf optimal lter are shown to be complex conjugates for the general case of a nonstationary input with constant power. Such an optimal lter is compared to a minimum mean square e...

In this paper, a fairly general framework for reasoning from inconsistent propositional basesis defined. Variable forgetting is used as a basic operation for weakening pieces of infor-mation so as to restore consistency. The key notion is that of recoveries, which are sets ofvariables whose forgetting enables restoring consistency. Several criteria for defining pre-ferred recove...

We describe a density-adaptive reinforcement learning and a density-adaptive forgetting algorithm. This learning algorithm uses hybrid k-D/2k-trees to allow for a variable resolution partitioning and labelling of the input space. The density adaptive forgetting algorithm deletes observations from the learning set depending on whether subsequent evidence is available in a local region of the par...

System identification and parameter estimation are important to obtain information from systems which are difficult to model and that are usually presented as BlackBox models. This work presents a point to point parameter estimation of a generalized non-deterministic system, whose results are variable through time, by using an exponential Forgetting Factor (FF). An average approximation is used...

We introduce a new linearly constrained minimum variance (LCMV) beamformer that combines the set-membership (SM) technique with the conjugate gradient (CG) method, and develop a low-complexity adaptive filtering algorithm for beamforming. The proposed algorithm utilizes a CG-based vector and a variable forgetting factor to perform the dataselective updates that are controlled by a time-varying ...