12 citations · 12 across the 10 of their papers we have counts for
12 papers
AnyEdit: A Unified Framework for Speech and Singing Voice Editing with Real-World Environmental Consistency
Yunjia Zhang, Junan Zhang, Jing Yang +3
We present AnyEdit, a unified, separation-free framework for speech and singing voice editing that preserves real-world acoustic environments, including noise, reverberation, and b…
EmoTrace-Bench: Diagnosing Emotional Expression in Text-to-Video Generation through Controlled Cues and Intensity
Yeheng Wang, Liudan Shi, Yi Zheng +5
Text-to-video (T2V) models are increasingly used to generate videos with targeted emotional expressions, yet existing evaluation methods mainly rely on holistic video-text alignmen…
Diff-Symbo: Text-Controlled Long-Duration Symbolic Music Generation Using Autoregressive Latent Diffusion Model
Zhiwei Lin, Jun Chen, Boshi Tang +5
Text-controlled symbolic music generation has recently gained research attention due to its versatile, flexible and straightforward approach to music composition. However, previous…
P-MUSE: Prompt-MIDI-Optional Model for Unified Instrumental Music Synthesis and Editing
Chong Jing, Junan Zhang, Jing Yang +3
MIDI-to-Music system renders the melody and rhythm of a target MIDI sequence into musical segment while cloning instrument timbre from a prompt recording. Existing systems typicall…
Anysynth:Zero-Shot Instrument Cloning via In-Context Learning and Asymmetric Hierarchical Guidance
Chong Jing, Junan Zhang, Jing Yang +3
Zero-shot instrument cloning aims to render an arbitrary [Target MIDI] sequence with the acoustic identity of an unseen instrument given only a short [Reference Audio, Reference MI…
Schrödinger Bridge Mamba for One-Step Speech Enhancement
Jing Yang, Sirui Wang, Chao Wu +2
We present Schrödinger Bridge Mamba (SBM), a novel model for efficient speech enhancement by integrating the Schrödinger Bridge (SB) training paradigm and the Mamba architecture. E…
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…
Multi-view MidiVAE: Fusing Track- and Bar-view Representations for Long Multi-track Symbolic Music Generation
Zhiwei Lin, Jun Chen, Boshi Tang +7
Variational Autoencoders (VAEs) constitute a crucial component of neural symbolic music generation, among which some works have yielded outstanding results and attracted considerab…