This paper presents a statistical approach to sound texture modeling based on a singular value analysis of spectral features or an eigenvector analysis of their similarity matrix. Using dimension reduction techniques we perform grouping of the signal into similar sounding audio segments that are recurrent in time. The method allows an automatic segmentation of audio signal into larger groups of...

In this paper, we propose a novel technique for video summarization based on the Singular Value Decomposition (SVD). For the input video sequence, we create a feature-frame matrix A, and perform the SVD on it. From this SVD, we are able to not only derive the reened feature space to better cluster visually similar frames, but also deene a metric to measure the amount of visual content contained...

In this paper we study properties of H-singular values of a positive tensor m n n R 1  × × ∈  and present an iterative algorithm for computing the largest H-singular value of the positive tensor. We prove that this method converges for any positive tensors.

Singular value decomposition has been used in signal processing, image processing, principal component analysis, robotics and my other real time applications. These applications demand fast processing of large datasets. SVD needs large amount of computation. In this paper, we present the parallel implementation of Singular Value Decomposition in FGPA. SVD is implemented using two sided Jacobi a...

Singular boundary conditions are formulated for non-selfadjoint Sturm-Liouville operators with singularities and turning points. For boundary value problems with singular boundary conditions properties of the spectrum are studied and the completeness of the system of root functions is proved.

High complexity of lattice construction algorithms and uneasy way of visualising lattices are two important problems connected with the formal concept analysis. Algorithm complexity plays significant role when computing all concepts from a huge incidence matrix. In this paper we try to modify an incidence matrix using matrix decomposition, creating a new matrix with fewer dimensions as an input...

Let A be an m × n matrix with m ≥ n. Then one form of the singular-value decomposition of A is A = UΣV, where U and V are orthogonal and Σ is square diagonal. That is, UUT = Irank(A), V V T = Irank(A), U is rank(A)×m, V is rank(A)× n and Σ =   σ1 0 · · · 0 0 0 σ2 · · · 0 0 .. .. . . . .. .. 0 0 · · · σrank(A)−1 0 0 0 · · · 0 σrank(A)   is a rank(A)× rank(A) diagonal matrix. In add...

This paper proposes a novel signature based on singular value decomposition (SVD) for video sequence matching. By considering the input image as a matrix, a partition procedure is first performed to separate the matrix into non-overlapping sub-images of a fixed size. The SVD process then individually decomposes each partitioned sub-image into an singular value and the corresponding singular vec...

Two numerical methods one continuous and the other discrete are proposed for solving inverse singular value problems The rst method consists of solving an ordinary di erential equation obtained from an explicit calculation of the projected gradient of a certain objective function The second method generalizes an iterative process proposed originally by Friedland et al for solving inverse eigenv...

This contribution reviews the external and the internal representations of linear time-invariant systems. This is done both in the time and the frequency domains. The realization problem is then discussed. Given the importance of norms in robust control and model reduction, the final part of this contribution is dedicated to the definition and computation of various norms. Again, the interplay ...