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MuseFlow-TSE: Music-Aware Target Speech Extraction via Cross-Source Band-Splitting and Data-to-Data Flow Matching

2026-10-21 03:03:10
Lu Ma, Yongliang Wang, Zhewen Su, Xin Tian
arXiv_SD
         
Abstract

Target speech extraction (TSE) isolates a speaker's voice given an enrollment utterance, but existing work is validated almost exclusively without music. Discriminative methods are content-faithful yet masking-bounded; generative methods improve naturalness but may hallucinate, drift in identity, and require many steps. We present MuseFlow-TSE, a discriminative deletion, generative insertion framework for music-bearing scenes. Stage-1 is a music-aware cross-source band-split extractor that estimates target, interfering speakers, music, and noise under conservation and music-leakage constraints. Stage-2 is a data-to-data flow refiner initialized by the discriminative estimate, negatively conditioned on interference stems, and run for 1--4 difficulty-adaptive Euler steps with an end-to-end fidelity objective.

Abstract (translated)

URL

https://arxiv.org/abs/2610.29119

PDF

https://arxiv.org/pdf/2610.29119.pdf

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