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The paper introduces a singing voice conversion (SVC) system for fine-grained style control, tackling style leakage and dynamic rendering challenges. They use a boundary-aware Whisper bottleneck to extract linguistic content, a technique matrix for dynamic style rendering with F0 processing, and a high-frequency band completion strategy using a 48kHz SVC model. Evaluated on the SVCC2025 dataset, their system achieved state-of-the-art naturalness with less training data compared to other top submissions.
Achieve state-of-the-art naturalness in singing voice conversion by decoupling linguistic content and style with a boundary-aware information bottleneck and high-frequency band completion, even with limited data.
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 motivated high-frequency band completion strategy that leverages an auxiliary standard 48kHz SVC model to augment the high-frequency spectrum, thereby overcoming data scarcity without overfitting. In the official SVCC2025 subjective evaluation, our system achieves the best naturalness performance among all submissions while maintaining competitive results in speaker similarity and technique control, despite using significantly less extra singing data than other top-performing systems. Audio samples are available online.
