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Title: Emotion recognition from physiological indicators for musical applications
Author: Jaimovich, Javier
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2013
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This thesis investigates the emotional response of audiences to music via physiological indicators, with the goal of creating interfaces that use emotional stale for music control, specifically in performance scenarios via electrodermal and cardiovascular measures. In the past two decades, multiple disciplines have shown interest in studying the relationship between music, emotion, and its physiological manifestation. However, despite the increasing attention, the actual mechanisms on how music modulates human emotion and how this correlates with physiological changes are still not well understood. Therefore, this topic provides interesting challenges to determine if musical emotions can be measured from audiences in ecological environments via physiological signals, In order to address this, there are several questions that need to be resolved; including how to measure physiological indicators of emotion in concert environments, what level of shared responses and variance can be expected from public audiences, and how to assess the induction of musical emotions on listeners. In order to answer these questions, the wor\( in this thesis starts by measuring physiological indicators of emotion in music concerts, revealing high correlations between the physiology of performers and audience members, as well as associations between physiological changes and structural and acoustical features of the music. In order to assess fell emotion on listeners and how these are manifested via changes in physiology, a series of modular public listening experiments (Emotion in Motion) were implemented in Dublin and New York, collecting physiological data and self-report measures of emotion from over 4000 participants. Analysis of this database reveals a set of specific physiological indicators that show significant relationships with musically induced emotions. This thesis also contributes robust feature extraction tools for EDA and HR, and a methodology for synchronization of multimodal signals for musical performance research .
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available