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Issue No.12 - December (2008 vol.30)
pp: 2067-2083
ABSTRACT
This paper investigates the potential of physiological signals as reliable channels for emotion recognition. All essential stages of an automatic recognition system are discussed, from the recording of a physiological dataset to a feature-based multiclass classification. In order to collect a physiological dataset from multiple subjects over many weeks, we used a musical induction method which spontaneously leads subjects to real emotional states, without any deliberate lab setting. Four-channel biosensors were used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to find the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by classification results. Classification of four musical emotions (positive/high arousal, negative/high arousal, negative/low arousal, positive/low arousal) is performed by using an extended linear discriminant analysis (pLDA). Furthermore, by exploiting a dichotomic property of the 2D emotion model, we develop a novel scheme of emotion-specific multilevel dichotomous classification (EMDC) and compare its performance with direct multiclass classification using the pLDA. Improved recognition accuracy of 95\% and 70\% for subject-dependent and subject-independent classification, respectively, is achieved by using the EMDC scheme.
INDEX TERMS
Signal analysis, synthesis, and processing, Methodologies and techniques, User/Machine Systems, Theory and methods, Interaction styles, Human-centered computing, Classifier design and evaluation, Feature evaluation and selection, Pattern analysis, Signal processing, Robotics
CITATION
Jonghwa Kim, Elisabeth André, "Emotion Recognition Based on Physiological Changes in Music Listening", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.30, no. 12, pp. 2067-2083, December 2008, doi:10.1109/TPAMI.2008.26
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