A Framework for Analyzing Emotional Expression in Contemporary Music Performance

Thesis Abstract

Contemporary music performance increasingly emphasizes expressive nuance, yet a comprehensive analytical framework for systematically assessing emotional expression remains underdeveloped. This study addresses the critical gap by proposing an innovative framework that integrates auditory, visual, and contextual cues to analyze emotional expression in live contemporary music performances. The primary aim is to develop, empirically validate, and demonstrate the utility of a multidimensional analytical model capable of capturing the complex interplay of expressive elements that convey emotion to audiences. Specific objectives include identifying key expressive parameters across performance modalities, formulating a conceptual model that encapsulates these parameters, and empirically testing the model's reliability and validity through a series of performance analyses. The research employs a mixed-methods design combining qualitative and quantitative approaches. The qualitative component involves thematic analysis of performance recordings and interview data to identify salient expressive features, informed by theories such as Baumgartner’s Model of Emotional Expression in Music and the Enactive Approach to Embodied Cognition. The quantitative phase involves assessing the consistency and predictive power of the proposed framework through statistical analyses. The target population comprises 60 professional performers from contemporary music ensembles and 20 expert judges specializing in performance analysis within a metropolitan city. A purposive sampling technique is employed to ensure selection of performers demonstrating varied stylistic approaches. Data collection instruments include high-definition video and audio recordings of performances, semi-structured interviews with performers, and a structured rating scale developed for the study. To ensure validity and reliability, the rating scale undergoes pilot testing and evaluation of inter-rater reliability using Intraclass Correlation Coefficients (ICC). Content validity of the interview protocols is established through expert review, while study procedures follow ethical guidelines for informed consent, confidentiality, and voluntary participation. Data analysis involves a combination of thematic analysis for qualitative data, followed by statistical techniques such as multiple regression analysis and factor analysis to identify the most salient expressive features and their relationships with perceived emotional impact. The validity of the analytical framework is tested through cross-validation methods and confirmatory factor analysis, while descriptive statistics are used to profile performance characteristics. Expected findings include the identification of core expressive parameters, such as dynamic variations, physical gestures, and contextual cues, which significantly influence audience perception of emotional expression. The framework is anticipated to demonstrate high reliability, with statistically significant correlations between measured expressive features and audience-rated emotional impact scores. Findings are expected to validate the theoretical assumptions underpinning Baumgartner’s and the Enactive models, offering a nuanced understanding of how contemporary performers communicate emotion through multiple expressive channels. This study contributes significantly to the theoretical and practical knowledge base by providing a validated, multidimensional model for analyzing emotional expression in music performance, which can be utilized by performers, educators, and researchers for performance evaluation, pedagogical development, and further scholarly inquiry. It advances current understanding by explicitly integrating auditory, visual, and contextual determinants of emotional communication within a cohesive analytical framework. The main conclusion emphasizes the importance of a systematic, multidimensional approach to understanding expressivity in contemporary music. Recommendations include adopting the framework in pedagogical settings to enhance emotional delivery skills and extending the research to cross-cultural contexts for broader applicability. Future studies might explore automation of expressive feature detection using emerging technologies such as machine learning and computer vision, extending the framework’s utility in digital and virtual performance environments.