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Erschienen in: Cognitive Computation 2/2014

01.06.2014

Classification of Music-Induced Emotions Based on Information Fusion of Forehead Biosignals and Electrocardiogram

verfasst von: Mohsen Naji, Mohammd Firoozabadi, Parviz Azadfallah

Erschienen in: Cognitive Computation | Ausgabe 2/2014

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Abstract

Emotion recognition systems have been developed to assess human emotional states during different experiences. In this paper, an approach is proposed for recognizing music-induced emotions through the fusion of three-channel forehead biosignals (the left temporalis, frontalis, and right temporalis channels) and an electrocardiogram. The classification of four emotional states in an arousal–valence space (positive valence/low arousal, positive valence/high arousal, negative valence/high arousal, and negative valence/low arousal) was performed by employing two parallel support vector machines as arousal and valence classifiers. The inputs of the classifiers were obtained by applying a fuzzy-rough model feature evaluation criterion and sequential forward floating selection algorithm. An average classification accuracy of 88.78 % was achieved, corresponding to an average valence classification accuracy of 94.91 % and average arousal classification accuracy of 93.63 %. The proposed emotion recognition system may be useful for interactive multimedia applications or music therapy.

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Metadaten
Titel
Classification of Music-Induced Emotions Based on Information Fusion of Forehead Biosignals and Electrocardiogram
verfasst von
Mohsen Naji
Mohammd Firoozabadi
Parviz Azadfallah
Publikationsdatum
01.06.2014
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 2/2014
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-013-9239-7

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