SCISSOR: Score-Conditioned Instrument Source Separation for Orchestral Recordings

Abstract:Orchestral separation recovers instrument sections from mixtures in which shared pitches, harmonics, and timbres obscure source identity. An aligned score provides instrument labels, note pitches, and activity times. A score-informed approach appends piano rolls to audio features before mask prediction. We introduce SCISSOR (Score-Conditioned Instrument Source Separation for Orchestral Recordings), which uses the score to form a frame-wise query for each source. Each query matches a shared audio representation, and a softmax over instrument and background slots jointly assigns overlapping time-frequency evidence. The queries retain instrument identity even when notes are missing from the score. After training on SynthSOD and a small set of URMP and PHENICX-Anechoic recordings, SCISSOR achieves the highest average SDR on held-out real recordings. With SynthSOD-only training, it leads on SynthSOD and zero-shot PHENICX-Anechoic, and improves on its audio-only control on zero-shot URMP. SCISSOR also degrades less under score corruption than the evaluated score-based baselines.

5 pages, 1 figure, 3 tables. Submitted to ICASSP 2027

Sound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)

arXiv:2609.33265 [cs.SD]

(or arXiv:2609.33265v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2609.33265 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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