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...

We study the basic problem of robust subspace recovery. That is, we assume a data set that some of its points are sampled around a fixed subspace and the rest of them are spread in the whole ambient space, and we aim to recover the fixed underlying subspace. We first estimate “robust inverse sample covariance” by solving a convex minimization procedure; we then recover the subspace by the botto...

This paper proposes two new algorithms for the direction of arrival (DOA) estimation of P radiating sources. Unlike the classical subspace-based methods, they do not resort to the eigen-decomposition of the covariance matrix of the received data. Indeed, the proposed algorithms involve the building of the signal subspace from the Krylov subspace of order P associated with the covariance matrix ...

In this note, we explain the implementation detail of multigrid methods. We will use the approach by space decomposition and subspace correction method; see Chapter: Subspace Correction Method and Auxiliary Space Method. The matrix formulation will be obtained naturally, when the functions’ basis representation is inserted. We also include a simplified implementation of multigrid methods using ...

The aim of this paper is to examine a numerical method for the computation of approximate solution of the continuous-time algebraic Riccati equation using Krylov subspace matrix. First of all, Global Arnoldi process is initiated to construct an orthonormal basis. In addition, Krylov subspace matrix is employed as projection method because it is one of the frequently referred method in the liter...

We discuss a new method for the iterative computation of some of the generalized singular values and vectors of a large sparse matrix. Our starting point is the augmented matrix formulation of the GSVD. The subspace expansion is performed by (approximately) solving a Jacobi–Davidson type correction equation, while we give several alternatives for the subspace extraction. Numerical experiments i...

Obtaining a good similarity matrix is extremely important in subspace clustering. Current state-of-the-art methods learn the through self-expressive strategy. However, these directly adopt original samples as set of basis to represent itself linearly. It difficult accurately describe linear relation between real-world applications, and thus hard find an ideal matrix. To better samples, we prese...

We consider a two-directional Krylov subspace Kk(A[j], b[j]), where besides the dimensionality k of the subspace increases, the matrix A[j] and vector b[j] which induce the subspace may also augment. Specifically, we consider the case where the matrix A[j] and the vector b[j] are augmented by block triangular bordering. We present a two-directional Arnoldi process to efficiently generate a sequ...

The paper deals with Automatic Speech Recognition system (ASR) with focus on isolated digits recognition in Slovak language. The paper discusses reduction of dimension of feature space. There are applied two ways dimension reductions. The first way feature subspace reduction is bases on manually selection some coefficients from feature matrix. The second way feature subspace reduction is automa...

In this paper, we propose a class of fast sequential bi-iteration singular value (Bi-SVD) subspace tracking algorithms for adaptive eigendecomposition of the cross covariance matrix in the recursive instrumental variable (RIV) method of system identification. These algorithms can be used for RIV subspace processing of signals in unknown correlated Gaussian noise. Realizations with O(Nr) and O(N...