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The paper trains an audio-language model to act as a critic of specific vocal-mix problems, such as identifying an issue, distinguishing opposing issues, and ranking severity, rather than just giving general mixing advice. Its evaluation is built on a curriculum of 11 issues because professional labels are scarce, so the results depend on how well synthetic severity levels match real studio judgment. The menu-selection result, where the critic favors candidates acoustically closer to a professional reference, is a useful signal but a narrow one.
MixJudge: Learning a Language-Queryable Critic for Vocal Mixing
arXiv:2610.30783v1 Announce Type: cross Abstract: Mixing requires not only applying processing but also assessing the resulting mix. Audio-language models can provide conversa- tional mixing advice, but existing evaluations do not separately test whether they can judge specific mixing issues. Since professional supervision for such judgments is scarce, we construct a curriculum of 11 vocal-mix issues at multiple severity levels and use it to train MixJudge, a language-queryable audio critic. MixJudge shows its ability to retrieve mixing issues, distinguish opposite issues, and order the severity of an issue. Moreover, its judgments transfer to menu selection: given a set of processing candidates, MixJudge often favors versions acoustically closer to a held-out professional version, demonstrating how explicit mixing judgment can support comparison and downstream mixing decisions.
Abstract:Mixing requires not only applying processing but also assessing the resulting mix. Audio-language models can provide conversa- tional mixing advice, but existing evaluations do not separately test whether they can judge specific mixing issues. Since professional supervision for such judgments is scarce, we construct a curriculum of 11 vocal-mix issues at multiple severity levels and use it to train MixJudge, a language-queryable audio critic. MixJudge shows its ability to retrieve mixing issues, distinguish opposite issues, and order the severity of an issue. Moreover, its judgments transfer to menu selection: given a set of processing candidates, MixJudge often favors versions acoustically closer to a held-out professional version, demonstrating how explicit mixing judgment can support comparison and downstream mixing decisions.
Sound (cs.SD); Computation and Language (cs.CL)
arXiv:2610.30783 [cs.SD]
(or arXiv:2610.30783v1 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2610.30783 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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