4 papers
FabasedVC: Enhancing Voice Conversion with Text Modality Fusion and Phoneme-Level SSL Features
Wenyu Wang, Zhetao Hu, Yiquan Zhou +4
In voice conversion (VC), it is crucial to preserve complete semantic information while accurately modeling the target speaker's timbre and prosody. This paper proposes FabasedVC t…
AVENet: Disentangling Features by Approximating Average Features for Voice Conversion
Wenyu Wang, Yiquan Zhou, Jihua Zhu +3
Voice conversion (VC) has made progress in feature disentanglement, but it is still difficult to balance timbre and content information. This paper evaluates the pre-trained model…
UIPE: Enhancing LLM Unlearning by Removing Knowledge Related to Forgetting Targets
Wenyu Wang, Mengqi Zhang, Xiaotian Ye +3
Large Language Models (LLMs) inevitably acquire harmful information during training on massive datasets. LLM unlearning aims to eliminate the influence of such harmful information…
SYKI-SVC: Advancing Singing Voice Conversion with Post-Processing Innovations and an Open-Source Professional Testset
Yiquan Zhou, Wenyu Wang, Hongwu Ding +4
Singing voice conversion aims to transform a source singing voice into that of a target singer while preserving the original lyrics, melody, and various vocal techniques. In this p…