Kavli Affiliate: Dan Luo
| First 5 Authors: Wei Chen, Wei Chen, , ,
| Summary:
Singing Voice Conversion (SVC) transfers a source singer’s timbre to a target
while keeping melody and lyrics. The key challenge in any-to-any SVC is
adapting unseen speaker timbres to source audio without quality degradation.
Existing methods either face timbre leakage or fail to achieve satisfactory
timbre similarity and quality in the generated audio. To address these
challenges, we propose DAFMSVC, where the self-supervised learning (SSL)
features from the source audio are replaced with the most similar SSL features
from the target audio to prevent timbre leakage. It also incorporates a dual
cross-attention mechanism for the adaptive fusion of speaker embeddings,
melody, and linguistic content. Additionally, we introduce a flow matching
module for high quality audio generation from the fused features. Experimental
results show that DAFMSVC significantly enhances timbre similarity and
naturalness, outperforming state-of-the-art methods in both subjective and
objective evaluations.
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