Seminar of Institute of Information Systems and Applications

Speaker : Prof. Jia-lien Hsu, Fu Jen Catholic University

Title: Affective content analysis of music emotion through EEG

Date : 15：00 – 16：30 Thursday 26-Oct-2017

Venue : 資電館R132（EE-CS Building R132）

Host : Prof. Von-Wun Soo

Abstract:

Emotion recognition of music objects is a promising and important research issues in the field of music information retrieval. Usually, music emotion recognition could be considered as a training/classification problem. However, even given a benchmark (a training data with ground truth) and using effective classification algorithms, music emotion recognition remains a challenging problem. Most previous relevant work focuses only on acoustic music content without considering individual difference (i.e., personalization issues). In addition, assessment of emotions is usually self-reported (e.g., emotion tags) which might introduce inaccuracy and inconsistency. Electroencephalography (EEG) is a non-invasive brainmachine interface which allows external machines to sense neurophysiological signals from the brain without surgery. The unintrusive EEG signals, captured from the central nervous system, have been utilized for exploring emotions. This paper proposes an evidence-based and personalized model for music emotion recognition. In the training phase for model construction and personalized adaption, we construct two predictive and generic models, trained by an artificial neural network. In the testing phase, given a music object, the processing steps are:

(1) to extract features from the music audio content,

(2) to apply ANN to calculate the vector in the arousal-valence emotion space, and

(3) to apply the transformation matrix to determine the personalized emotion vector.

Moreover, with respect to a moderate music object, we apply a sliding window on the music object to obtain a sequence of personalized emotion vectors, in which those predicted vectors will be fitted and organized as an emotion trail for revealing dynamics in the affective content of music object. Experimental results suggest the proposed approach is effective.

All faculties and students are welcome to join.