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This paper introduces Diptych, an AI-assisted system for reference listening in music production that allows users to define comparison scope at granular levels and inspect AI-interpreted audio features. Through a user study and expert evaluation, Diptych was shown to surface significant, expert-validated differences, improving clarity and actionable guidance for musicians. The findings advocate for AI comparison tools that emphasize user-defined scope, transparent evidence, and supportive, rather than definitive, AI interpretations.
AI-driven music comparison tools can effectively surface subtle, expert-validated differences by letting users precisely scope comparisons and inspect AI-interpreted features.
Reference listening is a common strategy in music production, but current comparison tools often obscure a key human judgment: deciding what should be compared. We present Diptych, an AI-assisted system that lets users define comparison scope across whole tracks or independently selected segments, while inspecting structured audio features and scope-specific AI interpretations. We evaluated Diptych in a within-participants study with 12 musicians, complemented by source-blinded ratings from four expert listeners. Participants used the system to surface additional differences, nine of ten of which received at least partial expert support, and reported good usability and greater clarity about possible next steps. These findings suggest that AI support for creative comparison should prioritize user-defined scope, inspectable evidence, and actionable guidance, while avoiding authoritative judgments that exceed what the evidence can support.
