62 citations · 129 across the 10 of their papers we have counts for
10 papers
MBTFNet: Multi-Band Temporal-Frequency Neural Network For Singing Voice Enhancement
Weiming Xu, Zhouxuan Chen, Zhili Tan +5
A typical neural speech enhancement (SE) approach mainly handles speech and noise mixtures, which is not optimal for singing voice enhancement scenarios. Music source separation (M…
DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting
Shubo Lv, Xiong Wang, Sining Sun +2
Real-world complex acoustic environments especially the ones with a low signal-to-noise ratio (SNR) will bring tremendous challenges to a keyword spotting (KWS) system. Inspired by…
Two-stage Neural Network for ICASSP 2023 Speech Signal Improvement Challenge
Mingshuai Liu, Shubo Lv, Zihan Zhang +6
In ICASSP 2023 speech signal improvement challenge, we developed a dual-stage neural model which improves speech signal quality induced by different distortions in a stage-wise div…
spatial-dccrn: dccrn equipped with frame-level angle feature and hybrid filtering for multi-channel speech enhancement
Shubo Lv, Yihui Fu, Yukai Jv +4
Recently, multi-channel speech enhancement has drawn much interest due to the use of spatial information to distinguish target speech from interfering signal. To make full use of s…
S-DCCRN: Super Wide Band DCCRN with learnable complex feature for speech enhancement
Shubo Lv, Yihui Fu, Mengtao Xing +5
In speech enhancement, complex neural network has shown promising performance due to their effectiveness in processing complex-valued spectrum. Most of the recent speech enhancemen…
Uformer: A Unet based dilated complex & real dual-path conformer network for simultaneous speech enhancement and dereverberation
Yihui Fu, Yun Liu, Jingdong Li +4
Complex spectrum and magnitude are considered as two major features of speech enhancement and dereverberation. Traditional approaches always treat these two features separately, ig…
DCCRN+: Channel-wise Subband DCCRN with SNR Estimation for Speech Enhancement
Shubo Lv, Yanxin Hu, Shimin Zhang +1
Deep complex convolution recurrent network (DCCRN), which extends CRN with complex structure, has achieved superior performance in MOS evaluation in Interspeech 2020 deep noise sup…
F-T-LSTM based Complex Network for Joint Acoustic Echo Cancellation and Speech Enhancement
Shimin Zhang, Yuxiang Kong, Shubo Lv +2
With the increasing demand for audio communication and online conference, ensuring the robustness of Acoustic Echo Cancellation (AEC) under the complicated acoustic scenario includ…
AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario
Yihui Fu, Luyao Cheng, Shubo Lv +10
In this paper, we present AISHELL-4, a sizable real-recorded Mandarin speech dataset collected by 8-channel circular microphone array for speech processing in conference scenario.…
DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement
Yanxin Hu, Yun Liu, Shubo Lv +6
Speech enhancement has benefited from the success of deep learning in terms of intelligibility and perceptual quality. Conventional time-frequency (TF) domain methods focus on pred…