Abstract
In the construction of computational models to recognize emotional state, emotion reporting continuously in time is essential based on the assumption that emotional responses of a human to certain stimuli could vary over time. However, currently existing methods to annotate emotion in temporal continuous fashion are confronting various types of challenges. Therefore, the manipulation of the annotated emotion prior to labeling training samples is necessary. In this work, we present an early attempt to manipulate the emotion annotated in arousal-valence space by applying three different signal filtering techniques to smooth annotation data; moving average filter, Savitzky-Golay filter, and me-dian filter. We conducted experiments of emotion recognition in music listening tasks employing brainwave signals recorded from an electroencephalogram (EEG). Smoothed annotation data were used to label the features extracted from EEG signals to train emotion recognizers using classification and regression techniques. Our empirical results indicated the potential of the moving average filter that could increase the performance of emotion recognition evaluated in subject-independent fashion.
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Acknowledgment
This research is partially supported by the Center of Innovation Program from Japan Science and Technology Agency (JST), JSPS KAKENHI Grant Number 25540101, and the Management Expenses Grants for National Universities Corporations from the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT).
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Thammasan, N., Fukui, Ki., Numao, M. (2017). Application of Annotation Smoothing for Subject-Independent Emotion Recognition Based on Electroencephalogram. In: Numao, M., Theeramunkong, T., Supnithi, T., Ketcham, M., Hnoohom, N., Pramkeaw, P. (eds) Trends in Artificial Intelligence: PRICAI 2016 Workshops. PRICAI 2016. Lecture Notes in Computer Science(), vol 10004. Springer, Cham. https://doi.org/10.1007/978-3-319-60675-0_10
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DOI: https://doi.org/10.1007/978-3-319-60675-0_10
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