Distant Speaker Recognition Based on the Automatic Selection of Reverberant Environments Using GMMs

2009 Chinese Conference on Pattern Recognition ◽

10.1109/ccpr.2009.5343954 ◽

2009 ◽

Author(s):

Longbiao Wang ◽

Yoshiki Kishi ◽

Atsuhiko Kai

Keyword(s):

Speaker Recognition ◽

Automatic Selection ◽

Reverberant Environments ◽

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Automatic Selection of Reference Pages in Wikipedia for Improving Targeted Entities Disambiguation

10.3115/v1/e14-4021 ◽

2014 ◽

Author(s):

Takuya Makino

Keyword(s):

Automatic Selection ◽

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Automatic Selection of Classified Samples with the Help of Previous Land Cover Data

Geo-information Science ◽

10.3724/sp.j.1047.2012.00507 ◽

2012 ◽

Vol 14 (4) ◽

pp. 507-513

Author(s):

Kun LIU ◽

Xiaomei YANG ◽

Tao ZHANG

Keyword(s):

Automatic Selection ◽

Land Cover Data ◽

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Deep Learning Based Multi-Channel Speaker Recognition in Noisy and Reverberant Environments

10.21437/interspeech.2019-1428 ◽

2019 ◽

Author(s):

Hassan Taherian ◽

Zhong-Qiu Wang ◽

DeLiang Wang

Keyword(s):

Deep Learning ◽

Speaker Recognition ◽

Reverberant Environments

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A hardware structure for the automatic selection of multi-level thresholds in digital images

Pattern Recognition ◽

10.1016/0031-3203(92)90124-2 ◽

1992 ◽

Vol 25 (12) ◽

pp. 1517-1528 ◽

Author(s):

M.A. Sid-Ahmed

Keyword(s):

Digital Images ◽

Automatic Selection ◽

Multi Level ◽

Selection Of ◽

Hardware Structure

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Automatic selection of hidden neurons and weights in neural networks for data classification using hybrid particle swarm optimization, multi-verse optimization based on Lévy flight

Evolutionary Intelligence ◽

10.1007/s12065-021-00579-w ◽

2021 ◽

Author(s):

Rabab Bousmaha ◽

Reda Mohamed Hamou ◽

Abdelmalek Amine

Keyword(s):

Neural Networks ◽

Particle Swarm Optimization ◽

Particle Swarm ◽

Data Classification ◽

Swarm Optimization ◽

Hybrid Particle ◽

Hybrid Particle Swarm Optimization ◽

Automatic Selection ◽

Hidden Neurons ◽

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Automatic selection of heavy-tailed distributions-based synergy Henry gas solubility and Harris hawk optimizer for feature selection: case study drug design and discovery

Artificial Intelligence Review ◽

10.1007/s10462-021-10009-z ◽

2021 ◽

Author(s):

Mohamed Abd Elaziz ◽

Dalia Yousri

Keyword(s):

Feature Selection ◽

Drug Design ◽

Gas Solubility ◽

Automatic Selection ◽

Heavy Tailed Distributions ◽

Heavy Tailed ◽

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PrivGuard: Sensitivity Guided Anonymization based PPDM with Automatic Selection of Sensitive Attributes

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) ◽

10.1109/icaccs51430.2021.9441991 ◽

2021 ◽

Author(s):

V. Uma Rani ◽

M. Sreenivasa Rao

Keyword(s):

Automatic Selection ◽

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EZ rules: Automatic Selection of Statistical Control Rules for Laboratory Tests. Westgard QC, Inc. Madison, WI: Westgard QC, Inc., 2000, 154 pp., $295.00. ISBN 1-886958-14-9.

Clinical Chemistry ◽

10.1093/clinchem/48.3.594 ◽

2002 ◽

Vol 48 (3) ◽

pp. 594-595 ◽

Author(s):

Kristian Linnet

Keyword(s):

Laboratory Tests ◽

Statistical Control ◽

Control Rules ◽

Automatic Selection ◽

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ON THE AUTOMATIC SELECTION OF REANALYSIS TECHNIQUES IN MACHINE TOOL STRUCTURAL ELEMENT OPTIMIZATION

Engineering Optimization ◽

10.1080/03052158608902535 ◽

1986 ◽

Vol 10 (3) ◽

pp. 163-181

Author(s):

S. J. LEE ◽

S. G. KAPOOR

Keyword(s):

Machine Tool ◽

Structural Element ◽

Automatic Selection ◽

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Data characterization for automatic selection of valve stiction detection algorithms

52nd IEEE Conference on Decision and Control ◽

10.1109/cdc.2013.6760559 ◽

2013 ◽

Author(s):

Alexey Zakharov ◽

Elena Zattoni ◽

Lei Xie ◽

Octavio Pozo ◽

Sirkka-Liisa Jamsa-Jounela

Keyword(s):

Detection Algorithms ◽

Automatic Selection ◽

Valve Stiction ◽

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