Diptych: Scoped, AI-Interpreted Comparison for Reference Listening in Music Production

Diptych lets musicians define comparison scopes for reference listening, helping surface differences experts partially support while avoiding overreaching AI judgments.

Chongjun Zhong, Abhinaba Roy, Archishman Ghosh, Kejun Zhang, Dorien Herremans

Published Sep 30, 2026▲ 20 on Hugging Face arXiv ↗

72%

OverallHighly rated

OverallHighly ratedVote to see the scoreThe exact score shows once you've voted, so every vote is your own call. The first half of each home page shelf shows its scores.

Readers

–

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel8/20reviewers recommend it

lenient 3/5

medium 4/10

strict 1/5

AI panel?Vote to see what the 20 AI reviewers said

Panel consensus

Diptych makes a genuinely useful design claim in user-defined comparison scope and inspectable features, but its pilot study with twelve musicians, partial expert backing, and missing benchmarks leave its AI interpretations and real-world viability unproven.

Abstract

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.