“2020-05-25”版本间的差异

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(6位用户的8个中间修订版本未显示)
第6行: 第6行:
 
|Dong Wang
 
|Dong Wang
 
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* Some experiments with flow-based denoising: GMM-based noise model, spectrum flow criterion.
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* Some literature reivew with deep denoising methods
 
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* More literature reivew for denosing
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* More literature review/theory design for NDA.
 
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第17行: 第19行:
 
|Yunqi Cai
 
|Yunqi Cai
 
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* experiments on DSC flow ivector
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* experiments on DSC flow ivector constant logdet
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* investigate constant logdet NF flow
 
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第28行: 第32行:
 
|Zhiyuan Tang
 
|Zhiyuan Tang
 
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* DNF for asr.
 
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* Continue.
 
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第39行: 第43行:
 
|Lantian Li
 
|Lantian Li
 
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* Enroll-test mismatch (data)
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* DT-DNF for ASV.
 
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* Enroll-test mismatch (NL scoring)
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* DT-DNF for ASV.
 
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第50行: 第56行:
 
|Ying Shi
 
|Ying Shi
 
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* compute the result about flow denoise and glow denoise on SDR  PESQ fwSNR
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* train new flow with both postive samples and negative samples
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* train RPCA baseline
 
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* finish the result form with RPCA and the new flow
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* train an energy model
 
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第71行: 第80行:
 
|Yue Fan
 
|Yue Fan
 
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* Check cn2
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* Gun data preparation
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* Do exprintments on mdl-cn
 
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* Perform more experiments on gunshot recognition with speaker recognition
 
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第97行: 第108行:
 
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* Kaldi baseline experiments on cn2.
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* Get some information about speakers to analyze cn2.
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* Other experiments.
 
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2020年5月25日 (一) 00:03的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • Some experiments with flow-based denoising: GMM-based noise model, spectrum flow criterion.
  • Some literature reivew with deep denoising methods
  • More literature reivew for denosing
  • More literature review/theory design for NDA.
Yunqi Cai
  • experiments on DSC flow ivector
  • experiments on DSC flow ivector constant logdet
  • investigate constant logdet NF flow
Zhiyuan Tang
  • DNF for asr.
  • Continue.
Lantian Li
  • Enroll-test mismatch (data)
  • DT-DNF for ASV.
  • Enroll-test mismatch (NL scoring)
  • DT-DNF for ASV.
Ying Shi
  • compute the result about flow denoise and glow denoise on SDR PESQ fwSNR
  • train new flow with both postive samples and negative samples
  • train RPCA baseline
  • finish the result form with RPCA and the new flow
  • train an energy model
Haoran Sun
Yue Fan
  • Check cn2
  • Gun data preparation
  • Do exprintments on mdl-cn
  • Perform more experiments on gunshot recognition with speaker recognition
Jiawen Kang
  • Organize meta-learning code
  • Check cn2
  • Prepare shared PPT
  • Prepare cross-channel and near-far data.
Ruiqi Liu
  • Kaldi baseline experiments on cn2.
  • Get some information about speakers to analyze cn2.
  • Other experiments.
Sitong Cheng
Zhixin Liu
Haolin Chen