نتایج جستجو برای: sequential extraction analysis casa
تعداد نتایج: 3014559 فیلتر نتایج به سال:
Robustness is one of the most important topics for automatic speech recognition (ASR) in practical applications. Monaural speech separation based on computational auditory scene analysis (CASA) offers a solution to this problem. In this paper, a novel system is presented to separate the monaural speech of two talkers. Gaussian mixture models (GMMs) and vector quantizers (VQs) are used to learn ...
The problem of segregating a sound source of interest from an acoustic background has been extensively studied due to applications in hearing prostheses, robust speech/speaker recognition and audio information retrieval. Computational auditory scene analysis (CASA) approaches the segregation problem by utilizing grouping cues involved in the perceptual organization of sound by human listeners. ...
Both genetic and environmental influences contribute to the wide variation in plasma von Willebrand factor (VWF) levels observed in humans. Inbred mouse strains also have highly variable plasma VWF levels, providing a convenient model in which to study genetic modifiers of VWF. Previously, we identified a major modifier of VWF levels in the mouse (Mvwf1) as a regulatory mutation in murine Galgt...
Tracking multiple targets in a cluttered environment is a challenging task. Probabilistic multiple hypothesis tracking (PMHT) is an efficient approach for dealing with it. Essentially PMHT is based on expectation-maximization for handling with association conflicts. Linearity in the number of targets and measurements is the main motivation for a further development and extension of this methodo...
Background extraction is a crucial step in many automatic video content analysis applications. In this study, we propose new tracking approach by usage of two sequential images in limited period and by giving the specific threshold; then if the difference is greater than the threshold, the extracted image is classified as foreground image while if it is less than the threshold it classified as ...
Feature selection, representation and extraction are integral to statistical pattern recognition systems. Usually features are represented as vectors that capture expert knowledge of measurable discriminative properties of the classes to be distinguished. The feature selection process entails manual expert involvement and repeated experiments. Automatic feature selection is necessary when (i) e...
The paper studies the problem of analyzing user comments and reviews of products sold online. Analyzing such reviews and producing a summary of them is very useful to both potential customers and product manufacturers. By analyzing reviews, we mean to extract features of products (also called opinion features) that have been commented by reviewers and determine whether the opinions are positive...
The Karhunen-Loeve (KL) transform is an optimal method for approximating a set of vectors or images by a low dimensional subspace. The method provides the optimal partial KL basis, which minimizes the MSE between the given set of vectors and their projections on this basis. In computer vision it is used for a variety of tasks such as object recognition, motion estimation, visual learning and ob...
OBJECTIVE Unexpected post-extraction bleeding is often experienced in clinical practice. Therefore, determining the risk of post-extraction bleeding in patients receiving anticoagulant therapy prior to surgery is beneficial. This study aimed to verify whether the HAS-BLED score was useful in predicting post-extraction bleeding in patients taking warfarin. DESIGN Retrospective cohort study. ...
Exploring bone proteome is an important and challenging task for understanding the mechanisms of physiological/pathological process of bone tissue. However, classical methods of protein extraction for soft tissues and cells are not applicable for bone tissue. Therefore, method development of efficient protein extraction is critical for bone proteome analysis. We found in this study that the pro...
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