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Speaker identification is an important topic with relevance to various disciplines. This paper proposes a novel speaker identification system, which consists of two major components—feature extraction and sparse representation classifier (SRC). Although SRC has been utilized for many classification purposes, few studies have provided insight into the link between the commonly used speaker identification...
This paper proposes a novel deep convolutional neural network (CNN), called sparse coding convolutional neural network (SC-CNN), to address the problem of sound event recognition and retrieval task. Unlike the general framework of a CNN, in which feature learning process is performed hierarchically, the proposed framework models the whole memorizing procedures in the human brain, including encoding,...
This research proposes a novel Bayesian sparse representation (BSR) method along with extracting facial parameters of SIFT to create sparse dictionaries, which are invariant to rotation, scale, and shift. By using K-means and information theory, a new dictionary called extended dictionary is developed. Compared with conventional orthogonal matching pursuit (OMP) algorithm, the proposed system that...
In this work, we extend the study of compressive sensing on Signal Space CoSaMP with redundant dictionaries by utilizing the partially known information. Under the assumption that the signal of interest, with some known locations of the nonzero coefficients, has a sparse representation under some redundant dictionaries, we modify the Signal Space CoSaMP algorithm by incorporating the partially known...
This paper proposes a system to address the problem of visual speech recognition. The proposed system is based on visual lip movement recognition by applying video content analysis technique. Using spatiotemporal features descriptors, we extracted features from video containing visual lip information. A preprocessing step is employed by removing the noise and enhancing the contrast of images in every...
In this paper, we proposes a visual-based vehicle classification system, in which it involves visual feature representation and classification step. In the feature representation step, we present a center enhanced spatial pyramid matching (CE-SPM) to extract the feature from images. In this work, we defined additional region in the center of each images to calculate the histograms of visual words...
All standard video coders are based on the prediction plus transform representation of an image block, which predicts the current block using various intra- and inter-prediction modes and then represents the prediction error using a fixed orthonormal transform. We propose to directly represent a mean-removed block using a redundant dictionary consisting of all possible inter-prediction candidates...
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