16 citations · 20 across the 14 of their papers we have counts for
14 papers
Video-to-Audio Generation with Fine-grained Temporal Semantics
Yuchen Hu, Yu Gu, Chenxing Li +2
With recent advances of AIGC, video generation have gained a surge of research interest in both academia and industry (e.g., Sora). However, it remains a challenge to produce tempo…
Mixture of Experts Fusion for Fake Audio Detection Using Frozen wav2vec 2.0
Zhiyong Wang, Ruibo Fu, Zhengqi Wen +10
Speech synthesis technology has posed a serious threat to speaker verification systems. Currently, the most effective fake audio detection methods utilize pretrained models, and in…
DPI-TTS: Directional Patch Interaction for Fast-Converging and Style Temporal Modeling in Text-to-Speech
Xin Qi, Ruibo Fu, Zhengqi Wen +12
In recent years, speech diffusion models have advanced rapidly. Alongside the widely used U-Net architecture, transformer-based models such as the Diffusion Transformer (DiT) have…
Text Prompt is Not Enough: Sound Event Enhanced Prompt Adapter for Target Style Audio Generation
Chenxu Xiong, Ruibo Fu, Shuchen Shi +9
Current mainstream audio generation methods primarily rely on simple text prompts, often failing to capture the nuanced details necessary for multi-style audio generation. To addre…
EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion Transformer
Jiarui Hai, Yong Xu, Hao Zhang +4
We introduce EzAudio, a text-to-audio (T2A) generation framework designed to produce high-quality, natural-sounding sound effects. Core designs include: (1) We propose EzAudio-DiT,…
Towards Diverse and Efficient Audio Captioning via Diffusion Models
Manjie Xu, Chenxing Li, Xinyi Tu +4
We introduce Diffusion-based Audio Captioning (DAC), a non-autoregressive diffusion model tailored for diverse and efficient audio captioning. Although existing captioning models r…
STA-V2A: Video-to-Audio Generation with Semantic and Temporal Alignment
Yong Ren, Chenxing Li, Manjie Xu +4
Visual and auditory perception are two crucial ways humans experience the world. Text-to-video generation has made remarkable progress over the past year, but the absence of harmon…
Video-to-Audio Generation with Hidden Alignment
Manjie Xu, Chenxing Li, Xinyi Tu +5
Generating semantically and temporally aligned audio content in accordance with video input has become a focal point for researchers, particularly following the remarkable breakthr…
Prompt-guided Precise Audio Editing with Diffusion Models
Manjie Xu, Chenxing Li, Duzhen zhang +3
Audio editing involves the arbitrary manipulation of audio content through precise control. Although text-guided diffusion models have made significant advancements in text-to-audi…
Jointly Recognizing Speech and Singing Voices Based on Multi-Task Audio Source Separation
Ye Bai, Chenxing Li, Hao Li +2
In short video and live broadcasts, speech, singing voice, and background music often overlap and obscure each other. This complexity creates difficulties in structuring and recogn…
MM-LLMs: Recent Advances in MultiModal Large Language Models
Duzhen Zhang, Yahan Yu, Jiahua Dong +4
In the past year, MultiModal Large Language Models (MM-LLMs) have undergone substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs via cost-effecti…
Multi-Task Audio Source Separation
Lu Zhang, Chenxing Li, Feng Deng +1
The audio source separation tasks, such as speech enhancement, speech separation, and music source separation, have achieved impressive performance in recent studies. The powerful…
Speaker and Direction Inferred Dual-channel Speech Separation
Chenxing Li, Jiaming Xu, Nima Mesgarani +1
Most speech separation methods, trying to separate all channel sources simultaneously, are still far from having enough general- ization capabilities for real scenarios where the n…
Single-channel Speech Dereverberation via Generative Adversarial Training
Chenxing Li, Tieqiang Wang, Shuang Xu +1
In this paper, we propose a single-channel speech dereverberation system (DeReGAT) based on convolutional, bidirectional long short-term memory and deep feed-forward neural network…