activity
20232026
most citedMossFormer2: Combining Transformer and RNN-Free Recurrent Network for Enhanced Time-Domain Monaural Speech Separation

1 citations · 1 across the 10 of their papers we have counts for

collaborators

10 papers

eess.AS2026

Beyond Lips: Integrating Gesture and Lip Cues for Robust Audio-visual Speaker Extraction

Zexu Pan, Xinyuan Qian, Shengkui Zhao +2

Most audio-visual speaker extraction methods rely on synchronized lip recording to isolate the speech of a target speaker from a multi-talker mixture. However, in natural human com…

eess.AS2025

Online Audio-Visual Autoregressive Speaker Extraction

Zexu Pan, Wupeng Wang, Shengkui Zhao +4

This paper proposes a novel online audio-visual speaker extraction model. In the streaming regime, most studies optimize the audio network only, leaving the visual frontend less ex…

eess.AS2025

Plug-and-Play Co-Occurring Face Attention for Robust Audio-Visual Speaker Extraction

Zexu Pan, Shengkui Zhao, Tingting Wang +4

Audio-visual speaker extraction isolates a target speaker's speech from a mixture speech signal conditioned on a visual cue, typically using the target speaker's face recording. Ho…

cs.SD2025

Multi-band Frequency Reconstruction for Neural Psychoacoustic Coding

Dianwen Ng, Kun Zhou, Yi-Wen Chao +3

Achieving high-fidelity audio compression while preserving perceptual quality across diverse content remains a key challenge in Neural Audio Coding (NAC). We introduce MUFFIN, a fu…

cs.SD2025

InspireMusic: Integrating Super Resolution and Large Language Model for High-Fidelity Long-Form Music Generation

Chong Zhang, Yukun Ma, Qian Chen +12

We introduce InspireMusic, a framework integrated super resolution and large language model for high-fidelity long-form music generation. A unified framework generates high-fidelit…

cs.SD2025

Conditional Latent Diffusion-Based Speech Enhancement Via Dual Context Learning

Shengkui Zhao, Zexu Pan, Kun Zhou +3

Recently, the application of diffusion probabilistic models has advanced speech enhancement through generative approaches. However, existing diffusion-based methods have focused on…

cs.SD2025

HiFi-SR: A Unified Generative Transformer-Convolutional Adversarial Network for High-Fidelity Speech Super-Resolution

Shengkui Zhao, Kun Zhou, Zexu Pan +3

The application of generative adversarial networks (GANs) has recently advanced speech super-resolution (SR) based on intermediate representations like mel-spectrograms. However, e…

eess.AS2024

Phonetic Enhanced Language Modeling for Text-to-Speech Synthesis

Kun Zhou, Shengkui Zhao, Yukun Ma +7

Recent language model-based text-to-speech (TTS) frameworks demonstrate scalability and in-context learning capabilities. However, they suffer from robustness issues due to the acc…

cs.SD2023★ 1 cited

MossFormer2: Combining Transformer and RNN-Free Recurrent Network for Enhanced Time-Domain Monaural Speech Separation

Shengkui Zhao, Yukun Ma, Chongjia Ni +7

Our previously proposed MossFormer has achieved promising performance in monaural speech separation. However, it predominantly adopts a self-attention-based MossFormer module, whic…

eess.AS2023

SPGM: Prioritizing Local Features for enhanced speech separation performance

Jia Qi Yip, Shengkui Zhao, Yukun Ma +8

Dual-path is a popular architecture for speech separation models (e.g. Sepformer) which splits long sequences into overlapping chunks for its intra- and inter-blocks that separatel…