137 citations · 201 across the 19 of their papers we have counts for
19 papers
Masked Audio Modeling with CLAP and Multi-Objective Learning
Yifei Xin, Xiulian Peng, Yan Lu
Most existing masked audio modeling (MAM) methods learn audio representations by masking and reconstructing local spectrogram patches. However, the reconstruction loss mainly accou…
Low-latency Speech Enhancement via Speech Token Generation
Huaying Xue, Xiulian Peng, Yan Lu
Existing deep learning based speech enhancement mainly employ a data-driven approach, which leverage large amounts of data with a variety of noise types to achieve noise removal fr…
QDFormer: Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic Decomposition
Xiang Li, Jinglu Wang, Xiaohao Xu +4
Audiovisual segmentation (AVS) is a challenging task that aims to segment visual objects in videos according to their associated acoustic cues. With multiple sound sources and back…
ABC-KD: Attention-Based-Compression Knowledge Distillation for Deep Learning-Based Noise Suppression
Yixin Wan, Yuan Zhou, Xiulian Peng +2
Noise suppression (NS) models have been widely applied to enhance speech quality. Recently, Deep Learning-Based NS, which we denote as Deep Noise Suppression (DNS), became the main…
DasFormer: Deep Alternating Spectrogram Transformer for Multi/Single-Channel Speech Separation
Shuo Wang, Xiangyu Kong, Xiulian Peng +3
For the task of speech separation, previous study usually treats multi-channel and single-channel scenarios as two research tracks with specialized solutions developed respectively…
Contrast-PLC: Contrastive Learning for Packet Loss Concealment
Huaying Xue, Xiulian Peng, Yan Lu
Packet loss concealment (PLC) is challenging in concealing missing contents both plausibly and naturally when there are only limited available context to use. Recently deep-learnin…
Time-Variance Aware Real-Time Speech Enhancement
Chengyu Zheng, Yuan Zhou, Xiulian Peng +2
Time-variant factors often occur in real-world full-duplex communication applications. Some of them are caused by the complex environment such as non-stationary environmental noise…
Improving Speech Enhancement via Event-based Query
Yifei Xin, Xiulian Peng, Yan Lu
Existing deep learning based speech enhancement (SE) methods either use blind end-to-end training or explicitly incorporate speaker embedding or phonetic information into the SE ne…
Real-time speech enhancement with dynamic attention span
Chengyu Zheng, Yuan Zhou, Xiulian Peng +2
For real-time speech enhancement (SE) including noise suppression, dereverberation and acoustic echo cancellation, the time-variance of the audio signals becomes a severe challenge…
Disentangled Feature Learning for Real-Time Neural Speech Coding
Xue Jiang, Xiulian Peng, Yuan Zhang +1
Recently end-to-end neural audio/speech coding has shown its great potential to outperform traditional signal analysis based audio codecs. This is mostly achieved by following the…
Cross-Scale Vector Quantization for Scalable Neural Speech Coding
Xue Jiang, Xiulian Peng, Huaying Xue +2
Bitrate scalability is a desirable feature for audio coding in real-time communications. Existing neural audio codecs usually enforce a specific bitrate during training, so differe…
Multi-Modal Multi-Correlation Learning for Audio-Visual Speech Separation
Xiaoyu Wang, Xiangyu Kong, Xiulian Peng +1
In this paper we propose a multi-modal multi-correlation learning framework targeting at the task of audio-visual speech separation. Although previous efforts have been extensively…
Towards Error-Resilient Neural Speech Coding
Huaying Xue, Xiulian Peng, Xue Jiang +1
Neural audio coding has shown very promising results recently in the literature to largely outperform traditional codecs but limited attention has been paid on its error resilience…
Latent-Domain Predictive Neural Speech Coding
Xue Jiang, Xiulian Peng, Huaying Xue +2
Neural audio/speech coding has recently demonstrated its capability to deliver high quality at much lower bitrates than traditional methods. However, existing neural audio/speech c…
End-to-End Neural Speech Coding for Real-Time Communications
Xue Jiang, Xiulian Peng, Chengyu Zheng +3
Deep-learning based methods have shown their advantages in audio coding over traditional ones but limited attention has been paid on real-time communications (RTC). This paper prop…
Phoneme-based Distribution Regularization for Speech Enhancement
Yajing Liu, Xiulian Peng, Zhiwei Xiong +1
Existing speech enhancement methods mainly separate speech from noises at the signal level or in the time-frequency domain. They seldom pay attention to the semantic information of…
Interactive Speech and Noise Modeling for Speech Enhancement
Chengyu Zheng, Xiulian Peng, Yuan Zhang +2
Speech enhancement is challenging because of the diversity of background noise types. Most of the existing methods are focused on modelling the speech rather than the noise. In thi…
End-to-End United Video Dehazing and Detection
Boyi Li, Xiulian Peng, Zhangyang Wang +2
The recent development of CNN-based image dehazing has revealed the effectiveness of end-to-end modeling. However, extending the idea to end-to-end video dehazing has not been expl…
An All-in-One Network for Dehazing and Beyond
Boyi Li, Xiulian Peng, Zhangyang Wang +2
This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed based on a re-formulated a…