Controllable Singing Style Conversion with Boundary-Aware Information Bottleneck
This paper presents the submission of the S4 team to the Singing Voice Conversion Challenge 2025 (SVCC2025)-a novel singing style conversion system that advances fine-grained style conversion and control within in-domain settings. To address the critical challenges of style leakage, dynamic rendering, and high-fidelity generation with limited data, we introduce three key innovations: a boundary-aware Whisper bottleneck that pools phoneme-span representations to suppress residual source style while preserving linguistic content; an explicit frame-level technique matrix, enhanced by targeted F0 processing during inference, for stable and distinct dynamic style rendering; and a perceptually ...
| Authors | Zhetao Hu, Yiquan Zhou, Wenyu Wang et al. |
|---|---|
| Primary category | cs.SD |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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