We present a new QRD-based technique for optimal multichannel filtering and its application to multi–channel acoustic noise reduction. A recursively calculated adaptive filter optimally estimates the speech component in a noisy signal. The complexity of this algorithm is an order of magnitude than that of existing optimal filtering based algorithms which are mainly based upon SVD– decomposition...

Abstract: The problem of recursive filtering for linear discrete-time systems with uncertainties is considered. A new suboptimal filtering algorithm is herein proposed. It is based on the fusion formula, which represents an optimal mean-square linear combination of local Kalman estimates with weights depending on cross-covariances between local filtering errors. In contrast to the optimal weigh...

Particle filtering techniques are a set of powerful and versatile simulation-based methods to perform optimal state estimation in nonlinear non-Gaussian state-space models. If the model includes fixed parameters, a standard technique to perform parameter estimation consists of extending the state with the parameter to transform the problem into an optimal filtering problem. However, this approa...

Noise reduction, which aims at extracting the clean speech from noisy observations, has plenty of applications. It has attracted a considerable amount of research attention over the past several decades. Although many methods have been developed, the most widely used one, by far, is the optimal linear filtering technique, which achieves clean speech estimate by passing the noisy observation thr...

In this paper novel compression techniques are developed for portable heart-monitoring equipment that could also form the basis for more intelligent diagnostic systems thanks to the way the compression algorithms depend on signal classification. There are two main categories of compression which are employed for electrocardiogram signals: lossless and lossy. Design of an optimal Wiener filter i...

While adaptive filtering is in principle intended for tracking non-stationary systems, most adaptive filtering algorithms have been designed for converging to a fixed unknown filter. When actually confronted with a non-stationary environment, they possess just one parameter (stepsize, forgetting factor) to adjust their tracking capability. Virtually the only existing optimal approach is the Kal...

State estimation problems for linear time-invariant systems with noisy inputs and outputs are considered. An efficient recursive algorithm for the smoothing problem is presented. The equivalence between the optimal filter and an appropriately modified Kalman filter is established. The optimal estimate of the input signal is derived from the optimal state estimate. The result shows that the nois...