“2024-09-02”版本间的差异

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|Qi Qu
 
|Qi Qu
 
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* KWS:
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** Cantonese train dataset collected and annotated: ~200 speakers, 10 keywords, 10 repeats per keyword.
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** More scene-specific test dataset collected: school, meeting, exhibition, etc.
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** Locating ideal keyword-wise thresholds for specific scenes using DCF (detection cost function).
 
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2024年9月2日 (一) 10:55的版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • Middle School AI book v0.0
Lantian Li
  • GPU status [1]
  • Apply for the AP title (Failed)
  • Submit two undergraduate thesis projects
Ying Shi
Zhenghai You
  • ICASSP exp and paper writing[2]
Junming Yuan
  • mixed Hubert pretraining v1 (in progress)
    • still have some bugs
Xiaolou Li
Zehua Liu
  • CNVSRC 2024 Website Finish
  • ICASSP exp and paper writing[3]
Pengqi Li
  • [4]Pondering the expected conclusions for paper
  • Experiment on timit(Finding and disadvantage)
    • poor consistency of TAO and LayerCAM methods
    • Toy Experiment
  • Challenge: Do the conclusions drawn from SID tasks (toy experiments) align with those from SV tasks (more SOTA models)?
  • To investigate and reproduce existing interpretability methods for verification task.
  • To analyze the importance of phones using TTS datasets(broad coverage of phonemes) based SOTA models
Wan Lin
  • ICASSP paper writing
Tianhao Wang
Zhenyu Zhou
  • check the conditional chain code
Junhui Chen
  • Writing Neural Scoring paper, 1st ver. done.
Jiaying Wang
Yu Zhang
  • reproduce iTransformer
  • Transfer iTrasnformer to financial data[5]
Wenqiang Du
  • Write middle school handbook(completed)
  • Continue to training Chinese and Cantonese KWS model
Yang Wei
  • Get familiar with text enroll KWS training
Lily
  • AI-Radiance's daily work
Turi
  • Working on dataset paper refinement & additional experiment
    • Attempting using pretrained model, not successful yet
Yue Gu
Qi Qu
  • KWS:
    • Cantonese train dataset collected and annotated: ~200 speakers, 10 keywords, 10 repeats per keyword.
    • More scene-specific test dataset collected: school, meeting, exhibition, etc.
    • Locating ideal keyword-wise thresholds for specific scenes using DCF (detection cost function).
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