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

Hugging Face Daily Papers Papers
music-production human-ai-interaction audio-analysis user-study creative-tools ai-interpreted-audio

Summary

Diptych is an AI-assisted reference-listening system for music production that lets users define comparison scope (whole tracks or segments) and inspect structured audio features with scope-specific AI interpretations, evaluated via a within-participant study with 12 musicians and expert ratings.

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

Source: https://huggingface.co/papers/2609.39963

Abstract

Referencelisteningisacommonstrategyinmusicproduction,butcurrentcomparisontoolsoftenobscureakeyhumanjudgment:decidingwhatshouldbecompared.WepresentDiptych,anAI-assistedsystemthatletsusersdefinecomparisonscopeacrosswholetracksorindependentlyselectedsegments,whileinspectingstructuredaudiofeaturesandscope-specificAIinterpretations.WeevaluatedDiptychinawithin-participantsstudywith12musicians,complementedbysource-blindedratingsfromfourexpertlisteners.Participantsusedthesystemtosurfaceadditionaldifferences,nineoftenofwhichreceivedatleastpartialexpertsupport,andreportedgoodusabilityandgreaterclarityaboutpossiblenextsteps.ThesefindingssuggestthatAIsupportforcreativecomparisonshouldprioritizeuser-definedscope,inspectableevidence,andactionableguidance,whileavoidingauthoritativejudgmentsthatexceedwhattheevidencecansupport.

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