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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)
In this paper, we propose an assessment method of agreement between fuzzy sets, called fuzzy Kappa which is deduced from the concept of Cohen's Kappa statistic. In fuzzy case, the Cohen's Kappa coefficient can be calculated generally by transforming the fuzzy sets into some crisp a-cut subsets. While the proposed fuzzy Kappa allows to directly evaluate an(More)
As bearer of high level semantics, audio signal is being more and more used in content-based multimedia retrieval. In this paper, we investigate the ball hit detection for sports games and propose a novel approach to detect ball hits. By employing Energy Peak Detection (EPD) and Mel Frequency Cepstral Coefficient-based (MFCC-based) Refinement (MBR), high(More)
A framework of fuzzy information fusion is proposed in this paper to automatically segment tumor areas of human brain from multispectral magnetic resonance imaging (MRI) such as T1-weighted, T2-weighted and proton density (PD) images. A priori knowledge about tumors described by radiology experts for different types of MRI are very helpful to guide a(More)
An information fusion based fuzzy segmentation method applied to Magnetic Resonance Images (MRI) is proposed in this paper. It can automatically extract the normal and abnormal tissues of human brain from multispectral images such as T1-weighted, T2-weighted and Proton Density (PD) feature images. Fuzzy models of normal tissues corresponding to three MRI(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)