23 citations · 31 across the 7 of their papers we have counts for
7 papers
Text-Queried Audio Source Separation via Hierarchical Modeling
Xinlei Yin, Xiulian Peng, Xue Jiang +2
Target audio source separation with natural language queries presents a promising paradigm for extracting arbitrary audio events through arbitrary text descriptions. Existing metho…
Universal Speech Token Learning via Low-Bitrate Neural Codec and Pretrained Representations
Xue Jiang, Xiulian Peng, Yuan Zhang +1
Current large speech language models are mainly based on semantic tokens from discretization of self-supervised learned representations and acoustic tokens from a neural codec, fol…
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…
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…