5 papers
SingVERSE: A Diverse, Real-World Benchmark for Singing Voice Enhancement
Shaohan Jiang, Junan Zhang, Yunjia Zhang +3
This paper presents a benchmark for singing voice enhancement. The development of singing voice enhancement is limited by the lack of realistic evaluation data. To address this gap…
DAFMSVC: One-Shot Singing Voice Conversion with Dual Attention Mechanism and Flow Matching
Wei Chen, Binzhu Sha, Dan Luo +4
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 timbre…
Multi-Metric Preference Alignment for Generative Speech Restoration
Junan Zhang, Xueyao Zhang, Jing Yang +3
Recent generative models have significantly advanced speech restoration tasks, yet their training objectives often misalign with human perceptual preferences, resulting in suboptim…
Singing Voice Conversion with Accompaniment Using Self-Supervised Representation-Based Melody Features
Wei Chen, Binzhu Sha, Jing Yang +3
Melody preservation is crucial in singing voice conversion (SVC). However, in many scenarios, audio is often accompanied with background music (BGM), which can cause audio distorti…
AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement
Junan Zhang, Jing Yang, Zihao Fang +5
We introduce AnyEnhance, a unified generative model for voice enhancement that processes both speech and singing voices. Based on a masked generative model, AnyEnhance is capable o…