PardonMix: Unpacking Ambiguity in Semantic Audio Mixing for Adaptive Interface Design

Abstract

Audio mixing requires sophisticated parameter control and accumulated listening experience. In collaborative workflows, novice musicians often struggle to convey their auditory intent to professional engineers, creating a semantic gap between abstract descriptors and technical execution. While prior studies have mapped descriptive terms to parameters by focusing on minimizing ambiguity, we propose that identifying types of ambiguity can serve as a key to bridging this communication gap. We conducted a formative study analyzing mixing parameter vectors from 10 professional engineers for 18 abstract descriptors, identifying distinct types of ambiguity: High Consensus, Intensity Variance, and Directional Divergence. We propose PardonMix, an adaptive interface strategy that dynamically allocates disambiguation widgets; direct automation for high consensus terms, degree control for intensity varying terms, and exploratory gallery for divergent terms. PardonMix seeks to bridge the semantic gap by embracing ambiguity, facilitating smoother collaboration that ensures the novice's artistic vision is precisely translated into technical execution.