25 citations · 39 across the 17 of their papers we have counts for
11 papers · 2 filters
Investigating training objective for flow matching-based speech enhancement
Liusha Yang, Ziru Ge, Gui Zhang +2
Speech enhancement(SE) aims to recover clean speech from noisy recordings. Although generative approaches such as score matching and Schrodinger bridge have shown strong effectiven…
Aliasing-Free Neural Audio Synthesis
Yicheng Gu, Junan Zhang, Chaoren Wang +3
In neural audio synthesis, neural vocoders and codecs are models that reconstruct waveforms from acoustic and latent representations, which are essential to the resulting audio qua…
AnyAccomp: Generalizable Accompaniment Generation via Quantized Melodic Bottleneck
Junan Zhang, Yunjia Zhang, Xueyao Zhang +1
Singing Accompaniment Generation (SAG) is the process of generating instrumental music for a given clean vocal input. However, existing SAG techniques use source-separated vocals a…
The CCF AATC 2025 Speech Restoration Challenge: A Retrospective
Junan Zhang, Mengyao Zhu, Xin Xu +3
Real-world speech communication is rarely affected by a single type of degradation. Instead, it suffers from a complex interplay of acoustic interference, codec compression, and, i…
TaDiCodec: Text-aware Diffusion Speech Tokenizer for Speech Language Modeling
Yuancheng Wang, Dekun Chen, Xueyao Zhang +3
Speech tokenizers serve as foundational components for speech language models, yet current designs exhibit several limitations, including: 1) dependence on multi-layer residual vec…
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…
Vevo2: A Unified and Controllable Framework for Speech and Singing Voice Generation
Xueyao Zhang, Junan Zhang, Yuancheng Wang +5
Controllable human voice generation, particularly for expressive domains like singing, remains a significant challenge. This paper introduces Vevo2, a unified framework for control…
SingNet: Towards a Large-Scale, Diverse, and In-the-Wild Singing Voice Dataset
Yicheng Gu, Chaoren Wang, Junan Zhang +4
The lack of a publicly-available large-scale and diverse dataset has long been a significant bottleneck for singing voice applications like Singing Voice Synthesis (SVS) and Singin…
Overview of the Amphion Toolkit (v0.2)
Jiaqi Li, Xueyao Zhang, Yuancheng Wang +9
Amphion is an open-source toolkit for Audio, Music, and Speech Generation, designed to lower the entry barrier for junior researchers and engineers in these fields. It provides a v…
Metis: A Foundation Speech Generation Model with Masked Generative Pre-training
Yuancheng Wang, Jiachen Zheng, Junan Zhang +3
We introduce Metis, a foundation model for unified speech generation. Unlike previous task-specific or multi-task models, Metis follows a pre-training and fine-tuning paradigm. It…
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…