ESP-3823

Published

2025-06-28

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Vol. 10 No. 6 (2025): Published

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Research Articles

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Copyright (c) 2025 Yifei Zhang

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How to Cite

Yifei Zhang. (2025). Mapping social psychology in AI-Driven music composition: Computational modeling through Pre- and Post-Analysis. Environment and Social Psychology, 10(6), ESP-3823. https://doi.org/10.59429/esp.v10i6.3823

Mapping social psychology in AI-Driven music composition: Computational modeling through Pre- and Post-Analysis

Yifei Zhang

Master's student Department of Composition and Conducting, Shanghai Conservatory of Music, shanghai, 200031, China


DOI: https://doi.org/10.59429/esp.v10i6.3823


Keywords: Computational music analysis; artificial intelligence; music composition; Pre-analysis; Post-analysis; environmental psychology; social psychology; feature extraction; style transfer; cross-cultural musical understanding

Abstract

Music composition, as a fundamental expression of human creativity and cultural identity, operates within complex environmental and social psychological frameworks that have profoundly shaped its evolutionary trajectory across millennia. This study systematically explores the application of computational music analysis and artificial intelligence technologies in music composition from environmental and social psychological perspectives, focusing on the technical implementation and psychological impacts of two critical stages: pre-analysis and post-analysis. Pre-analysis, functioning as a crucial environmental structuring process, provides creators with cognitive scaffolding mechanisms through computational techniques including feature extraction, music segmentation, and key and chord analysis, effectively reducing the inherent limitations of human information processing and transforming the traditional blank page into a structured creative space populated with meaningful possibilities and directional cues. This process not only expands creators' cognitive awareness of global musical traditions but also addresses critical social equity issues by lowering barriers to formal training and cultural exposure, thereby achieving democratized access to musical knowledge. Post-analysis operates as a reflective environmental restructuring process that enables creators to systematically examine completed works through style transfer, modification algorithms, and multi-dimensional evaluation tools, identifying structural relationships, harmonic patterns, and stylistic elements, thus creating evidence-based refinement opportunities that transcend the limitations of subjective self-assessment. Through case study analyses of classical works such as Ravel's "Boléro" and Coltrane's "Giant Steps," this research validates the effectiveness of computational analysis techniques in revealing complex musical structures and promoting cross-cultural understanding. The findings indicate that the integrated application of pre-analysis and post-analysis can significantly enhance creative efficiency, strengthen innovative capabilities, and facilitate stylistic fusion, while simultaneously facing challenges including over-reliance on technology, potential risks of creative homogenization, and concerns regarding cultural authenticity preservation. The significance of this research extends beyond the musical technology domain to encompass broader questions of human-computer interaction, cultural preservation and innovation, and the psychology of creativity in technologically-mediated environments, providing essential theoretical foundations and practical guidance for the future development of music composition, educational reform, and technological ethics.

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