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pro vyhledávání: '"He, Shulin"'
Autor:
Ye, Zhen, Ju, Zeqian, Liu, Haohe, Tan, Xu, Chen, Jianyi, Lu, Yiwen, Sun, Peiwen, Pan, Jiahao, Bian, Weizhen, He, Shulin, Liu, Qifeng, Guo, Yike, Xue, Wei
Recent progress in large-scale zero-shot speech synthesis has been significantly advanced by language models and diffusion models. However, the generation process of both methods is slow and computationally intensive. Efficient speech synthesis using
Externí odkaz:
http://arxiv.org/abs/2404.14700
Speech enhancement aims to improve speech quality and intelligibility, especially in noisy environments where background noise degrades speech signals. Currently, deep learning methods achieve great success in speech enhancement, e.g. the representat
Externí odkaz:
http://arxiv.org/abs/2402.14225
Target speaker extraction (TSE) aims to isolate a specific voice from multiple mixed speakers relying on a registerd sample. Since voiceprint features usually vary greatly, current end-to-end neural networks require large model parameters which are c
Externí odkaz:
http://arxiv.org/abs/2312.10979
Multi-channel speech enhancement utilizes spatial information from multiple microphones to extract the target speech. However, most existing methods do not explicitly model spatial cues, instead relying on implicit learning from multi-channel spectra
Externí odkaz:
http://arxiv.org/abs/2309.10393
Multi-channel speech enhancement seeks to utilize spatial information to distinguish target speech from interfering signals. While deep learning approaches like the dual-path convolutional recurrent network (DPCRN) have made strides, challenges persi
Externí odkaz:
http://arxiv.org/abs/2309.10379
Autor:
Chen, Jun, Rao, Wei, Wang, Zilin, Lin, Jiuxin, Ju, Yukai, He, Shulin, Wang, Yannan, Wu, Zhiyong
The previous SpEx+ has yielded outstanding performance in speaker extraction and attracted much attention. However, it still encounters inadequate utilization of multi-scale information and speaker embedding. To this end, this paper proposes a new ef
Externí odkaz:
http://arxiv.org/abs/2306.16250
Autor:
Liu, Wenzhe, Shi, Yupeng, Chen, Jun, Rao, Wei, He, Shulin, Li, Andong, Wang, Yannan, Wu, Zhiyong
This paper describes a real-time General Speech Reconstruction (Gesper) system submitted to the ICASSP 2023 Speech Signal Improvement (SSI) Challenge. This novel proposed system is a two-stage architecture, in which the speech restoration is performe
Externí odkaz:
http://arxiv.org/abs/2306.08454
Autor:
Ju, Yukai, Chen, Jun, Zhang, Shimin, He, Shulin, Rao, Wei, Zhu, Weixin, Wang, Yannan, Yu, Tao, Shang, Shidong
This paper introduces the Unbeatable Team's submission to the ICASSP 2023 Deep Noise Suppression (DNS) Challenge. We expand our previous work, TEA-PSE, to its upgraded version -- TEA-PSE 3.0. Specifically, TEA-PSE 3.0 incorporates a residual LSTM aft
Externí odkaz:
http://arxiv.org/abs/2303.07704
Target speaker extraction is to extract the target speaker, specified by enrollment utterance, in an environment with other competing speakers. Therefore, the task needs to solve two problems, speaker identification and separation, at the same time.
Externí odkaz:
http://arxiv.org/abs/2212.01106
Autor:
He, Shulin, Rao, Wei, Liu, Jinjiang, Chen, Jun, Ju, Yukai, Zhang, Xueliang, Wang, Yannan, Shang, Shidong
Most neural network speech enhancement models ignore speech production mathematical models by directly mapping Fourier transform spectrums or waveforms. In this work, we propose a neural source filter network for speech enhancement. Specifically, we
Externí odkaz:
http://arxiv.org/abs/2210.15853