The spatio-temporal-prediction (STP) method for multichannel speech enhancement has recently been proposed. This approach makes it theoretically possible to attenuate the residual noise without distorting speech. In addition, the STP method depends only on the second-order statistics and can be implemented using a simple linear filtering framework. Unfortunately, some numerical problems can ari...

The convergence properties of a recently developed recursive subspace identification algorithm are investigated in this paper. The algorithm operates on the basis of an extended instrumental variable (EIV) version of the propagator method for signal subspace estimation. It is proved that, under weak conditions on the input signal and the identified system, the considered MOESP class of recursiv...

The convergence properties of recently developed recursive subspace identification methods are investigated in this paper. The algorithms operate on the basis of instrumental variable (IV) versions of the propagator method for signal subspace estimation. It is proved that, under suitable conditions on the input signal and the system, the considered recursive subspace identification algorithms c...

In this paper the signal subspace approach for nonpara-metric speech enhancement is considered. Several algorithms have been proposed in the litterature but only partly analyzed. Here, the diierent algorithms are compared , and the emphasis is put onto the limiting factors and practical behavior of the estimators. Experimental results show that the signal subspace approach may lead to a signiic...

We discuss the DPASTd algorithm for signal subspace tracking. Our analysis shows that, under a sufficient condition on the step size, the DPASTd algorithm is locally stable even though delayed updating is applied. A pipelined realization of the algorithm and the corresponding systolic architecture are also proposed and a method for reciprocal computation is discussed. We also present simulation...

A novel subspace approach for speech enhancement using a subband whitening filter is proposed. Previous subspace approaches for enhancement either assumed white noise or used a fullband pre-whitening filter before enhancement for colored noise. The previous approaches were successful only in reducing the upper bound of the signal distortion while reducing noise. However, the proposed method min...

Subspace methods are essential to high-resolution environment sensing in the emerging unmanned systems, if further combined with millimeter-wave (mm-Wave) massive multi-input multi-output (MIMO) technique. The estimation of signal/noise subspace, as one critical step, is yet computationally complex and presents a particular challenge when developing low-complexity automotive radars. In this wor...

The objective of this paper is threefold: (1) to provide an extensive review of signal subspace speech enhancement, (2) to derive an upper bound for the performance of these techniques, and (3) to present a comprehensive study of the potential of subspace filtering to increase the robustness of automatic speech recognisers against stationary additive noise distortions. Subspace filtering method...

In this paper, the signal subspace approach for non-parametric speech enhancement is considered. Several algorithms have been proposed in the literature but only partly analyzed. Here, the different algorithms are compared, and the emphasis is put onto the limiting factors and practical behavior of the estimators. Experimental results show that the signal subspace approach may lead to a signifi...

We propose a new approach to the problem of compressive phase retrieval in which the goal is to reconstruct a sparse vector from the magnitude of a number of its linear measurements. The proposed framework relies on constrained sensing vectors and a two-stage reconstruction method that consists of two standard convex optimization programs that are solved sequentially. Various methods for compre...