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Emotional Impact of Source Localization in Music Using Machine Learning and EEG: a proof-of-concept study
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  • Eleonora De Filippi ,
  • Timothy Schmele ,
  • ARIJIT NANDI ,
  • Adan Garriga Torres ,
  • Alexandre Pereda-Banos
Eleonora De Filippi
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Timothy Schmele
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ARIJIT NANDI
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Adan Garriga Torres
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Alexandre Pereda-Banos
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Abstract

Little is currently known about how varied source locations affect a listener’s emotional reaction to music. Here, using spectral features extracted from electrophysiology (EEG) data, we tested through machine learning whether four music source positions (front, back, left, and right) could be accurately distinguished according to the type of valence in a subject-wise manner. The findings demonstrate that distinct EEG correlates can reliably classify the four source locations and that the effect is stronger when music with a negative emotional va- lence is played outside of the listener’s visual field. This proof-of-concept study may pave the way for advanced spatial audio analysis approaches in music infor- mation retrieval by considering the listener’s emotional impact depending on the source direction of incidence.