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Title: Clustering technique-based least square support vector machine
Page Link: Clustering technique-based least square support vector machine -
Posted By: seminar details
Created at: Saturday 09th of June 2012 02:42:45 PM
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Clustering technique-based least square support vector machine for EEG signal classification





Introduction

The electroencephalogram (EEG) signal is a measure of the
summed activity of approximately 100 millions of neurons
lying in the vicinity of the recording electrodes . It may provide
insight into the functional structure and dynamics of the
brain . Basically the EEG is complex and aperiodic time
series containing information of the electrical activity g ....etc

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Title: emotion recognition using speech ppt
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Created at: Monday 21st of January 2013 07:09:00 PM
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Title: fuzzy support vector machine matlab code
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Created at: Monday 23rd of February 2015 06:27:33 PM
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Title: speech emotion recognition ppt
Page Link: speech emotion recognition ppt -
Posted By:
Created at: Tuesday 02nd of July 2013 05:30:10 PM
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Title: fuzzy support vector machine matlab code
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Created at: Friday 15th of April 2016 01:08:12 PM
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Title: fuzzy support vector machine matlab code
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Created at: Wednesday 22nd of October 2014 11:01:15 AM
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Title: fuzzy support vector machine matlab code
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Created at: Friday 17th of June 2016 03:25:53 PM
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i do master degree about this topic.
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Title: fuzzy support vector machine matlab code
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Created at: Monday 02nd of February 2015 11:51:16 PM
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Title: human speech emotion recognition using svm ppt
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Created at: Sunday 30th of April 2017 05:03:25 PM
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For classifying the input speech as angry,sad,etc.

I have the mfcc extraction for the input and a database of speeches (train).

Have no idea how to proceed with svm coding. ....etc

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Title: Speech Emotion Recognition for Affective Human Robot Interaction full report
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Posted By: seminar topics
Created at: Sunday 14th of March 2010 11:38:23 PM
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Speech Emotion Recognition for Affective Human Robot Interaction

Abstract
We evaluate the performance of a speech emotion recognition method for affective human-robot interaction. In the proposed method, emotion is classified into 6 classes: Angry, bored, happy, neutral, sad, and surprised. After applying noise reduction and speech detection, we obtain a feature vector for an utterance from statistics of phonetic and prosodic information. The phonetic information includes log energy, shimmer, formant frequencies, and Teager energy; the pr ....etc

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