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Monaural singing voice separation has aroused considerable attention. Many pitch-based methods have been proposed to address this task, but generally have limited performance. The most crucial difficulties lie in the inaccurate judgment on voiced pitches and the failed recognition on unvoiced singing sounds. In this paper, we propose a novel algorithm based on the latent component analysis of time-frequency...
Transient interference can dramatically degrade the performance of over-the-horizon radar (OTHR). A novel transient interference suppression method based on structured low-rank matrix decomposition is proposed in this paper. Unlike most of the traditional interference suppression methods which need three steps: detect interferences, excise corrupted data and reconstruct excised data, the proposed...
In this paper, we present a novel approach to achieve blind stain decomposition in histo-pathology images. The method is based on stain color estimation, followed by stain absorbing vector generation and matrix computation. Unlike conventional approaches adopting linear processing algorithms to analyze chromatic information in the cylindrical-coordinate color spaces, which may be inappropriate for...
Multilinear analysis is pervasive in a wide variety of fields, ranging from Signal Processing to Chemometrics, and from Machine Vision to Data Mining. Determining the quality of a given tensor decomposition is a task of utmost importance that spans all fields of application of tensors. This task by itself is hard in its nature, since even determining the rank of a tensor is an NP-hard problem. Fortunately,...
We consider ill-posed linear inverse problems involving the estimation of structured sparse signals. When the sensing matrix has i.i.d. standard normal entries, there is a full-fledged theory on the sample complexity and robustness properties. In this work, we propose a way of making use of this theory to get good bounds for the i.i.d. Bernoulli ensemble. We first provide a deterministic relation...
In this paper, we propose a low-rank tensor deconvolution problem which seeks multiway replicative patterns and corresponding activating tensors of rank-1. An alternating least squares (ALS) algorithm has been derived for the model to sequentially update loading components and the patterns. In addition, together with a good initialisation method using tensor diagonalization, the update rules have...
Dolby TrueHD is a lossless and hierarchical audio coding format that not only enables compact bit-exact representation of the source multichannel audio signal, but also facilitates low complexity reconstruction of downmixes thereof. The dual objective is achieved by linear transformation of input channels into internal channels coded in the bitstream, via primitive matrices that are exactly invertible...
We study the problem of sequentially recovering a sparse vector xt and a vector from a low-dimensional subspace ℓt from knowledge of their sum mt = xt + ℓt. If the primary goal is to recover the low-dimensional subspace where the ℓt's lie, then the problem is one of online or recursive robust principal components analysis (PCA). To the best of our knowledge, this is the first correctness result for...
The problem of finding the sparse representation of a signal has attracted a lot of attention over the past years. In particular, uniqueness conditions and reconstruction algorithms have been established by relaxing a non-convex optimisation problem. The finite rate of innovation (FRI) theory is an alternative approach that solves the sparsity problem using algebraic methods based around Prony's algorithm...
Signal quality assessment is an important issue as noisy channels could mean lost information and unreliable data. In the field of Electrocardiograms (ECG), this is also important as noise could affect the detection of transient cardiac conditions which occur in these noisy channels. In VPW-FRI, the common annihilator is used to decompose multichannel signals with common root locations. Using information...
Recently, we have proposed a general adaptation scheme for deep neural network based on discriminant condition codes and applied it to supervised speaker adaptation in speech recognition based on either frame-level cross-entropy or sequence-level maximum mutual information training criterion [1, 2, 3, 4]. In this case, each condition code is associated with one speaker in data, which is thus called...
We consider the application of sparse-representation and robust-subspace-recovery techniques to detect abandoned objects in a target video acquired with a moving camera. In the proposed framework, the target video is compared to a previously acquired reference video, which is assumed to have no abandoned objects. The detection method explores the low-rank similarities among the reference and target...
The Canonical Polyadic (CP) tensor decomposition has become an attractive mathematical tool these last ten years in various fields. Yet, efficient algorithms are still lacking to compute the full CP decomposition, whereas rank-one approximations are rather easy to compute. We propose a new deflation-based iterative algorithm allowing to compute the full CP decomposition, by resorting only to rank-one...
It is well-known that the Ramanujan-sum cq(n) has applications in the analysis of periodicity in sequences. Recently the author developed a new type of Ramanujan-sum representation especially suited for finite duration sequences x(n): This is based on decomposing x(n) into a sum of signals belonging to so-called Ramanujan subspaces Sqi. This offers an efficient way to identify periodic components...
High dimensional data is often modeled as a linear combination of a sparse component, a low-rank component, and noise. An example is a video sequence of a busy scene where the background is the low-rank part and the foreground, e.g. moving pedestrians, is the sparse part. Sparse and low rank (SLR) matrix decomposition is a recentmethod that estimates those components. In this paper we develop an l...
This paper introduces a new PARAFAC algorithm for a class of third-order tensors. Particularly, the proposed algorithm is based on subspace estimation and solving a non-symmetrical joint diagonalization problem. To deal with large scale problem, a procedure for overcoming scale and permutation ambiguities is proposed in a parallel computing scheme leading to a significant cost reduction of our method...
We present a method for separating background and foreground optical flow fields induced by observer's egomotion and motion of objects, respectively. Optical flow is a vector field of instantaneous apparent motion computed from successive images. An optical flow field can be assumed as a linear combination with a few basis fields caused by translational and rotational egomotion and a spatially sparse...
A geometrical perspective is introduced that enables unification and generalization of several results regarding the distributions of quantities that arise in connection with an important class of multiple-channel detectors. Standard models on sets of normalized vectors following from joint Gaussian assumptions in this context are relaxed to the geometrically appealing model of uniform distributions...
This paper proposes a novel framework that makes it possible to realize non-negative matrix factorization (NMF)-like signal decompositions in the time-domain. This new formulation also allows for an extension to multi-resolution signal decomposition, which was not possible with the conventional NMF framework.
We consider dictionary-based signal decompositions with group sparsity, a variant of structured sparsity. We point out that the group sparsity-inducing constraint alone may not be sufficient in some cases when we know that some bigger groups or so-called supergroups cannot vanish completely. To deal with this problem we introduce the notion of relative group sparsity preventing the supergroups from...
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