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第177行： |
| | |Jiaying Wang | | |Jiaying Wang |
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| − | * | + | * weekly report |
| | + | * PIT baseline: ConTasNet (finish tonight) |
| | + | * test whether the separation target is the closer one to the cohort embedding: the rate is around 0.5 |
| | + | ** confused about the efficiency of cohort |
| | + | ** Further experiment:TasNet with minimal loss |
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| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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- Interspeech 2024 paper refinement
- Design/Discussion AI popular science
- Conjecture for minmum loss training
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| Lantian Li
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| Ying Shi
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- Finish INTERSPEECH paper
- Investigate random order SOT for multi-talker ASR task
- 3-mix 0s offset test condition
- DOM-SOT 20.51
- PIT-SOT 23.26
- random-order SOT 26.20
- group work
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| Zhenghai You
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- Weekly report
- Some evaluations about TSE speaker encoder
- Huawei project (Phase 1st)
- Some doubts about the paper due to the latest testing in minimum loss
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- Change the speakerbeam speaker encoder to frequency domain
- Train a SID with a speakerbeam structure
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| Junming Yuan
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- Finish INTERSPEECH paper
- Make the plan for the large vocabulary pretraining task.
- Focus on the experimental details of the few-shot paper from Google.
- Try to address the 3 questions:
- How to change MT pretraining model structure?
- How to train three strictly comparable pretraining models based on MT, Hubert, and wav2vec?
- Why does Hubert+MT perform significantly better?
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| Chen Chen
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- Finish IS24 paper
- Some documents for VTS X project
- Proposal for next stage work on VSR/VTS
- Focus on two task: 1) CNCVS2 dataset 2) Mandarin VSR Benchmark [1] on CNCVS1&2&CNVSRC
- Aim at a solid benchmark with data/code/model
- Perhaps a long journal paper
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- Conditional entropy analysis of VTS task
- MFA is done
- TODOs: feature/embedding extracting, clustering, discrete conditional entropy calculating
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| Xiaolou Li
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| Zehua Liu
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| Pengqi Li
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| Wan Lin
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| Tianhao Wang
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- Finish INTERSPEECH paper
- Code reorganization
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| Zhenyu Zhou
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- InterSpeech2024 submission
- Code reorganization
- Neuro scoring reviewing
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| Junhui Chen
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| Jiaying Wang
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- weekly report
- PIT baseline: ConTasNet (finish tonight)
- test whether the separation target is the closer one to the cohort embedding: the rate is around 0.5
- confused about the efficiency of cohort
- Further experiment:TasNet with minimal loss
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| Yu Zhang
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- Portfolio backtesting report
- stock trade API
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| Wenqiang Du
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- Aibabel
- Control Uyghur KWS model FA,but not get a good performance yet.
- Continue test and update CN KWS model
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| Yang Wei
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| Lily
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- Paper reading
- Prepare for overview paper
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| Turi
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- Data collection app[2]
- Course works
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