activity
20182021
most citedEnd-to-End Model for Speech Enhancement by Consistent Spectrogram Masking

5 citations · 8 across the 3 of their papers we have counts for

collaborators

6 papers

cs.SD2021

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.…

eess.SP20202 cited

Affine Combination of Diffusion Strategies over Networks

Danqi Jin, Jie Chen, Cedric Richard +2

Diffusion adaptation is a powerful strategy for distributed estimation and learning over networks. Motivated by the concept of combining adaptive filters, this work proposes a comb…

cs.LG20191 cited

Partial AUC optimization based deep speaker embeddings with class-center learning for text-independent speaker verification

Zhongxin Bai, Xiao-Lei Zhang, Jingdong Chen

Deep embedding based text-independent speaker verification has demonstrated superior performance to traditional methods in many challenging scenarios. Its loss functions can be gen…

eess.AS2019

Speaker Verification By Partial AUC Optimization With Mahalanobis Distance Metric Learning

Zhongxin Bai, Xiao-Lei Zhang, Jingdong Chen

Receiver operating characteristic (ROC) and detection error tradeoff (DET) curves are two widely used evaluation metrics for speaker verification. They are equivalent since the lat…

cs.SD20195 cited

End-to-End Model for Speech Enhancement by Consistent Spectrogram Masking

Xingjian Du, Mengyao Zhu, Xuan Shi +3

Recently, phase processing is attracting increasinginterest in speech enhancement community. Some researchersintegrate phase estimations module into speech enhancementmodels by usi…

eess.SP2018

Adaptive Parameters Adjustment for Group Reweighted Zero-Attracting LMS

Danqi Jin, Jie Chen, Cedric Richard +1

Group zero-attracting LMS and its reweighted form have been proposed for addressing system identification problems with structural group sparsity in the parameters to estimate. Bot…