MUUNRiver-Bench: Diagnosing Relation-Dependent Music Retrieval with Multimodal Instructions

Abstract:Music retrieval is relation-dependent: given a reference track, a listener may seek its style with a new theme, a cover, or a comparable voice, and these intents demand contradictory rankings. We present MUUNRiver-Bench, a diagnostic benchmark whose reference-audio queries use natural-language instructions to define relevance. A pipeline combining expert genre priors, LLM-generated prompts and lyrics, synthesis, and expert review yields 3,440 tracks spanning 13 genres and 116 sub-genres, and seven tasks: similar-music, style-preserving lyric-rewriting, lyric-preserving style-rewriting, cover, vocal-timbre, isolated-vocal, and segment retrieval. Across six models in eight configurations, task-wise rank reversals reveal complementary biases: acoustic encoders favour local identity, whereas text-aligned encoders favour semantic relations. Frozen encoders diagnose default similarity preferences; instruction-aware and audio-text fusion systems provide exploratory tests of textual conditioning, with neither simple fusion scheme consistently improving its backbone

Sound (cs.SD); Artificial Intelligence (cs.AI)

arXiv:2609.16090 [cs.SD]

(or arXiv:2609.16090v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2609.16090 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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