A Framework for Analyzing Audience Engagement in Live Electronic Music

Thesis Abstract

Live electronic music performances increasingly rely on dynamic audience interaction to shape perceived intensity, atmosphere, and temporal experience, yet there is limited consensus on how engagement should be defined, measured, and modeled across diverse venues and genres. This study addresses the gap by developing a comprehensive framework to analyze audience engagement in live electronic music, integrating physiological, behavioral, and perceptual dimensions to yield actionable insights for artists, producers, and event producers. The aim is to construct a theoretically grounded yet pragmatically usable framework that links performer actions, sonic features, environmental context, and audience responses to engagement outcomes. Specific objectives are to (1) synthesize conceptualizations of audience engagement from musicology, psychology, and human-computer interaction; (2) identify measurable indicators across three domains—physiological arousal (heart rate variability, skin conductance), behavioral engagement (facial expressions, movement, participation cues), and perceptual engagement (self-reported immersion, flow, and affect); (3) examine the moderating roles of genre, venue acoustics, lighting, and crowd density on engagement; (4) specify a multi-level model that relates sonic features (tempo, spectral centroid, rhythm complexity, dynamic range) and performer-interaction strategies (cueing, call-and-response, live processing) to engagement outcomes; and (5) validate the framework through a cross-site empirical study. The methodology employs a mixed-methods design in three phases. Phase 1 involves a theoretical synthesis and instrument development, drawing on the Flow Theory (Csikszentmihalyi) and the Affective Events Theory to ground the constructs, complemented by entrainment and social presence theory to explicate interactional dynamics. Phase 2 comprises a quasi-experimental field study across four representative live settings—two clubs and two festival stages—sampling 320 attendees and 40 performers across 12 performances. Physiological data will be collected from a subsample of 120 attendees via wearable sensors (photoplethysmography for heart rate variability, galvanic skin response) synchronized with high-resolution audio features captured by digital signal processing tools. Behavioral engagement will be coded from video recordings using a validated movement and gaze-motion taxonomy, and perceptual engagement will be assessed through post-performance questionnaires employing Likert-scale measures and open-ended prompts. Phase 3 uses hierarchical linear modeling (HLM) and structural equation modeling (SEM) to examine direct and indirect effects of sonic features and performer actions on engagement indicators, with moderation analyses for genre and venue characteristics. The study will also apply thematic analysis to interview data from 20 performers and 30 event staff to triangulate the framework components and identify emergent practices. Expected findings indicate that engagement is a multi-dimensional construct where physiological arousal, embodied behavior, and perceived immersion interact synergistically. Specific sonic features—higher rhythmic regularity coupled with moderate spectral brightness and dynamic range—are anticipated to predict higher reported immersion, moderated by crowd density and stage lighting. Performer interaction strategies that align with real-time feedback (e.g., adaptive filtering, live manipulation responding to audience motion) are expected to amplify social presence and collective flow, thereby elevating engagement scores across sites. The framework is anticipated to demonstrate robust cross-context validity, with some moderation by genre (e.g., techno versus bass music) and venue acoustics. The study contributes to knowledge by offering an integrative, operationalizable framework that connects sonic design, performer behavior, and environmental factors to measurable engagement outcomes in live electronic music, filling a gap in empirical, theory-driven models applicable across genres and settings. It advances methodological approaches by combining physiological sensing, computer-vision-based behavioral coding, and robust multilevel analyses, providing a replicable blueprint for future research and practice. Practical implications include evidence-based guidance for producers and venue designers on optimal acoustic and lighting configurations, tempo and dynamics strategies, and performer interaction techniques to foster sustained audience engagement. Recommendations for future research emphasize longitudinal studies of engagement trajectories, cross-cultural comparisons, and the applicability of the framework to hybrid and augmented-reality performance formats. The study concludes that a coherent, multi-dimensional framework is essential for understanding and enhancing audience engagement in live electronic music, with implications for artistic creation, event programming, and audience-care practices.