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Title: What strikes the strings of your heart? - multi-label dimensionality reduction for music emotion analysis via brain imaging
Authors: Liu, Y
Liu, Y 
Wang, C
Wang, X
Zhou, P
Yu, G 
Chan, KCC 
Keywords: Brain models
Feature extraction
Multiple signal classification
Smoothing methods
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on autonomous mental development, 2015, v. 7, no. 3, 7101270, p. 176-188 How to cite?
Journal: IEEE transactions on autonomous mental development 
Abstract: After 20 years of extensive study in psychology, some musical factors have been identified that can evoke certain kinds of emotions. However, the underlying mechanism of the relationship between music and emotion remains unanswered. This paper intends to find the genuine correlates of music emotion by exploring a systematic and quantitative framework. The task is formulated as a dimensionality reduction problem, which seeks the complete and compact feature set with intrinsic correlates for the given objectives. Since a song generally elicits more than one emotion, we explore dimensionality reduction techniques for multi-label classification. One challenging problem is that the hard label cannot represent the extent of the emotion and it is also difficult to ask the subjects to quantize their feelings. This work tries utilizing the electroencephalography (EEG) signal to solve this challenge. A learning scheme called EEG-based emotion smoothing (E2S) and a bilinear multi-emotion similarity preserving embedding (BME-SPE) algorithm are proposed. We validate the effectiveness of the proposed framework on standard dataset CAL-500. Several influential correlates have been identified and the classification via those correlates has achieved good performance. We build a Chinese music dataset according to the identified correlates and find that the music from different cultures may share similar emotions.
ISSN: 1943-0604
EISSN: 1943-0612
DOI: 10.1109/TAMD.2015.2429580
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