What Strikes the Strings of Your Heart? - Multi-Label Dimensionality Reduction for Music Emotion Analysis via Brain Imaging

Yang LIU*, Yan Liu, Chaoguang Wang, Xiaohong Wang, Peiyuan Zhou, Gino Yu, Keith C.C. Chan

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

16 Citations (Scopus)


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.

Original languageEnglish
Article number7101270
Pages (from-to)176-188
Number of pages13
JournalIEEE Transactions on Autonomous Mental Development
Issue number3
Publication statusPublished - Sept 2015

Scopus Subject Areas

  • Software
  • Artificial Intelligence

User-Defined Keywords

  • Brain models
  • Electroencephalography
  • Feature extraction
  • Multiple signal classification
  • Music
  • Smoothing methods


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