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| 第56行： |
第56行： |
| | |Ying Shi | | |Ying Shi |
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| − | * | + | * verify mel spectrum on DAE and double flow |
| | + | * train DAE and Double Flow(different layer and different flow type ) with spectrum feats(6 kinds of noise with random snr). |
| | + | * compute SDR and PESQ of DAE and Double Flow |
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| − | * | + | * verify the result |
| | + | * compute fwSNR |
| | + | * build more powerfull DAE baseline and flow model |
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| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
|
- Mostly completed the investigation on normalized scoring and complted the draft article
- Investigated the DNF with simulation data
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- Keep on investigating the DNF model with simulation data
- Coin some new approach for SID scoring, motivated by the NL interpertation of PLDA.
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| Yunqi Cai
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- Finished patent
- Investigate the total covariance constrain of DNF and showed some results
- Preparing the weekly paper report
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- More investigation on DNF
|
|
| Zhiyuan Tang
|
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| Lantian Li
|
- Submit DNF patent.
- Complete DNF repository.
- Start frame-level DNF-SRE training.
|
|
|
| Ying Shi
|
- verify mel spectrum on DAE and double flow
- train DAE and Double Flow(different layer and different flow type ) with spectrum feats(6 kinds of noise with random snr).
- compute SDR and PESQ of DAE and Double Flow
|
- verify the result
- compute fwSNR
- build more powerfull DAE baseline and flow model
|
|
| Wenqiang Du
|
- Using the add noise、 spec-argument 、attention to improve model performance in different device channel
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| Haoran Sun
|
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| Yue Fan
|
- Build the baseline models
- Learn to use kaldi
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- Optimize the baseline models
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| Jiawen Kang
|
- Collect cnceleb speaker-birth map.
- Finish length, age and 11*11 geners experiments.
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- Make some distribution figures for different speakers and geners.
- Design Cross-channel experiments.
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| Ruiqi Liu
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- Experiments on CN-Celeb different scenes and different length .
- Continue training the model.
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- More experiments for different speakers and scenes.
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| Sitong Cheng
|
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| Zhixin Liu
|
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| Haolin Chen
|
- Double WaveGlow on noisy TIMIT
- Test performance of Double Glow
- Optimize model structure
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- Comprehensive evaluation on two models
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