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automatic analysis may have many applications such as smart human-computer interactions or multimedia indexing. Main difficulties for an efficient speech emotion classification reside in complex emotional class borders leading to necessity of appropriate audio feature selection. While current work in the literature only relies on classical frequency and(More)
This paper deals with speech emotion analysis within the context of increasing awareness of the wide application potential of affective computing. Unlike most works in the literature which mainly rely on classical frequency and energy based features along with a single global classifier for emotion recognition, we propose in this paper some new harmonic and(More)
The purpose of this paper is to make an automatic classification of speech into seven emotional classes as anger, boredom, disgust, fear, gladness, neutral and sadness. A two-stage classification composed of several sub-classifiers is proposed. A feature set with 68 features has been computed over 286 speech samples from the Berlin database. The sequential(More)
Music mood present the inherent emotional state of music on certain duration of music segment. However, the mood may vary in the music pieces. Thus it is important to investigate the duration of music segments which can best present the stable mood states in music. Four versions of music datasets with duration of clips from 4 seconds to 32 seconds are(More)
Feature subset selection is an important subject when training classifiers in Machine Learning (ML) problems. Too many input features in a ML problem may lead to the so-called " curse of dimen-sionality " , which describes the fact that the complexity of the classifier parameters adjustment during training increases exponentially with the number of(More)
Feature subset selection is an important subject when training classifiers in Machine Learning (ML) problems. Too many input features in a ML problem may lead to the so-called "curse of dimensionality", which describes the fact that the complexity of the classifier parameters adjustment during training increases exponentially with the number of features.(More)