Understanding common practice techniques in audio mixing through Sonic Cartoon theory
Yong Ju Lee · 2025
This thesis investigates common-practice techniques in audio mixing, focusing on how mix engineers translate subjective, metaphorical language into concrete technical actions. It proposes an innovative, interdisciplinary approach that merges Sonic Cartoon Theory (SCT), Actor-Network Theory (ANT), and an ecological approach to perception to understand the complex relationship between language, perception, and technical manipulation in the mixing process. The research employs a mixed-methods approach, combining case studies of professional mixing sessions with comparative analysis of mixing strategies across different musical genres. Key areas of investigation include the role of metaphorical descriptions in shaping mixing decisions, the translation of these descriptions into adjustments in pitch, frequency, amplitude, or reverb, and the social dynamics of collaboration between engineers and artists. This study reveals that, despite stylistic variations, engineers employ remarkably consistent strategies, guided by the principles of SCT and ANT, to manipulate invariant acoustic properties in response to metaphorical language. These strategies center on shaping the perceived quantity, size, energy, materials, and spatial characteristics of sonic elements. It also highlights how the mixing process is a socio-technical negotiation, where artistic vision, technical expertise, and available tools interact. The research concludes with the development of “SonicMass,” a plugin designed to bridge the gap between subjective sonic descriptors and measurable acoustic parameters, offering a practical tool for both novice and experienced engineers. The research contributes to the field by: offering the first in-depth, empirical application of SCT to audio mixing; providing an integrated theoretical framework (SCT, ANT, ecological approach) for understanding the language-action interface in audio production; demonstrating the consistent mapping of metaphorical language onto specific mixing techniques; generating empirical insights into common mixing strategies; discussing the implications of AI-driven tools in the mixing process; and offering a pragmatic solution (SonicMass) to the challenges of translating subjective language into technical practice