Hidden Markov models

Results: 740



#Item
61HMM based Viterbi paths for rejection correction in a convolutional neural network classifier Hubert Cecotti, Szilard Vajda and Abdel Bela¨ıd READ Group LORIA/CNRS Campus Scientifique BPVandoeuvre-les-Nancy

HMM based Viterbi paths for rejection correction in a convolutional neural network classifier Hubert Cecotti, Szilard Vajda and Abdel Bela¨ıd READ Group LORIA/CNRS Campus Scientifique BPVandoeuvre-les-Nancy

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Source URL: www.dsi.unifi.it

Language: English - Date: 2009-12-17 04:49:47
62Automated recognition of bird song elements from continuous recordings using dynamic time warping and hidden Markov models: A comparative study Joseph A. Kogan and Daniel Margoliash Department of Organismal Biology and A

Automated recognition of bird song elements from continuous recordings using dynamic time warping and hidden Markov models: A comparative study Joseph A. Kogan and Daniel Margoliash Department of Organismal Biology and A

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Source URL: pondside.uchicago.edu

Language: English - Date: 2013-09-16 09:29:20
63Autonomous Fault Detection in Self-Healing Systems: Comparing Hidden Markov Models and Artificial Neural Networks Chris Schneider  University of St Andrews

Autonomous Fault Detection in Self-Healing Systems: Comparing Hidden Markov Models and Artificial Neural Networks Chris Schneider University of St Andrews

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Source URL: www.adambarker.org

Language: English - Date: 2014-01-30 12:05:49
64POS Tagging for German: How Important is the Right Context? ¨ Steliana Ivanova, Sandra Kubler Umbria Inc., Boulder, CO, Indiana University Bloomington, IN, USA ,  Abstract

POS Tagging for German: How Important is the Right Context? ¨ Steliana Ivanova, Sandra Kubler Umbria Inc., Boulder, CO, Indiana University Bloomington, IN, USA , Abstract

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Source URL: cl.indiana.edu

Language: English - Date: 2008-07-22 10:48:06
65Can the Computer Learn to Play Music Expressively? Christopher Raphael Department of Mathematics and Statistics, University of Massachusetts at Amherst, Amherst, MA,

Can the Computer Learn to Play Music Expressively? Christopher Raphael Department of Mathematics and Statistics, University of Massachusetts at Amherst, Amherst, MA,

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Source URL: music.informatics.indiana.edu

Language: English - Date: 2000-11-10 09:32:54
66Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction Stephen H. Bach  Bert Huang

Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction Stephen H. Bach Bert Huang

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Source URL: psl.umiacs.umd.edu

Language: English - Date: 2013-11-10 21:52:40
67Nonparametric Multi-group Membership Model for Dynamic Networks Jure Leskovec Stanford University Stanford, CA 94305

Nonparametric Multi-group Membership Model for Dynamic Networks Jure Leskovec Stanford University Stanford, CA 94305

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Source URL: i.stanford.edu

Language: English - Date: 2014-02-08 04:56:54
68A study of speaker adaptation for DNN-based speech synthesis Zhizheng Wu Pawel Swietojanski Christophe Veaux Steve Renals

A study of speaker adaptation for DNN-based speech synthesis Zhizheng Wu Pawel Swietojanski Christophe Veaux Steve Renals

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Source URL: homepages.inf.ed.ac.uk

Language: English - Date: 2015-12-17 18:06:23
69The Viterbi Algorithm  1 The Viterbi Algorithm. M. S. Ryan* and G. R. Nudd.

The Viterbi Algorithm 1 The Viterbi Algorithm. M. S. Ryan* and G. R. Nudd.

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Source URL: twiki.di.uniroma1.it

Language: English - Date: 2013-02-26 06:18:25
70Automatic Transcription of Piano Music  Automatic Transcription of Piano Music Christopher Raphael Univ. of Massachusetts, Amherst Department of Mathematics and Statistic

Automatic Transcription of Piano Music Automatic Transcription of Piano Music Christopher Raphael Univ. of Massachusetts, Amherst Department of Mathematics and Statistic

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Source URL: music.informatics.indiana.edu

Language: English - Date: 2002-08-10 14:15:12