A Framework for Analyzing Emotional Expression in Contemporary Electronic Music

Thesis Abstract

Contemporary electronic music plays a significant role in shaping modern auditory culture, yet the mechanisms through which it conveys and elicits specific emotional responses remain under-theorized and insufficiently empirically examined. This study addresses the gap by developing a comprehensive framework for analyzing emotional expression within this genre, with the aim of elucidating how various musical, technological, and contextual factors contribute to the perception and evocation of specific emotional states. The specific objectives include identifying salient musical features associated with emotional expression, examining listeners' emotional responses, and proposing an integrative model that links musical parameters with emotional outcomes. Utilizing a mixed-methods research design, the study combines quantitative analysis of musical features with qualitative insights into listener perceptions. The population comprised 150 electronic music tracks across subgenres such as ambient, IDM, dubstep, and trance, selected through stratified random sampling. Data collection involved a combination of acoustic feature extraction using MIRtoolbox software to quantify parameters such as tempo, dynamics, spectral flux, and rhythm complexity, alongside administering a structured survey to 300 participants drawn from electronic music communities. The survey assessed emotional responses using the Geneva Emotional Music Scale (GEMS) and collected demographic data, musical listening habits, and subjective emotional perceptions. Reliability of the instruments was confirmed through Cronbach’s alpha scores exceeding 0.85, and validity was established via expert reviews. Data analysis employed multiple regression analysis to identify significant predictors among musical features for specific emotional responses, supported by ANOVA tests to compare emotional ratings across subgenres. Thematic analysis was conducted on open-ended survey responses to capture nuanced emotional experiences unquantifiable through standardized scales. The study also applied the Structural Equation Modeling (SEM) technique to test the proposed theoretical framework, which synthesizes elements of Schubert’s Music and Emotion Theory and Juslin and Västfjäll’s Brunswik’s Lens Model, tailored specifically to electronic music's unique qualities. Expected findings suggest that rhythmic complexity and spectral flux significantly predict feelings of vitality and excitement, while tempo and harmonic minimalism correlate with feelings of calmness or melancholy. The SEM analysis is anticipated to confirm that musical features collectively influence emotional responses, mediated by listener variables such as familiarity and musical training. The results are expected to demonstrate that a combination of specific acoustic features and contextual factors create distinct emotional experiences, which the proposed framework successfully integrates into a coherent analytical model. This study contributes to knowledge by offering a validated, operationalized framework for analyzing emotional expression in electronic music, enriching both theoretical understanding and practical applications in music production, curation, and therapy. It advances existing models by incorporating digital signal processing metrics and listener perception data, thus facilitating multidimensional analysis. The findings will assist composers and sound designers in creating emotionally compelling electronic compositions aligned with desired listener responses. The main conclusion emphasizes the necessity for an interdisciplinary approach combining musicological, psychological, and technological perspectives to understand emotional expression in electronic music comprehensively. Recommendations include further empirical testing of the framework across diverse cultural contexts, exploration of real-time emotional response analysis using physiological measures, and application of the model in music therapy and affective computing. Future research should also examine how individual differences, such as personality traits and cultural background, modulate emotional perception within this genre, thereby broadening the applicability of the developed framework.