“2026-01-05”版本间的差异

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（10位用户的13个中间修订版本未显示）
第6行： 第6行：
 
|Dong Wang
 
|Dong Wang
 
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* English version of the AI handbook  - middle/high school almost done
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第63行： 第65行：
 
|Pengqi Li
 
|Pengqi Li
 
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* Draft Paper: The framework and core content are complete.
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* AI Handbook: Checking and verifying references.
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* Winter Camp Preparation: Preparing materials for the Tsinghua University Middle School AI Winter Camp.
 
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第74行： 第78行：
 
|Junming Yuan
 
|Junming Yuan
 
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* Aug-MT-HuBERT:
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** Fixed the pre-training to 100K steps, compared 3 noise ratios（50%, 80%, and 100%）and 2 different SNR sampling strategies([-3, 3] uniform / [0, 10] Gauss).
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*** Best setting, PR performance(PER): 8.12 -> 8.08, ASR performance(WER): 9.22 -> 9.07
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*** There is still around 3% gap compared to HuBERT.
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* SS adaptation(SI-SDRi):
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** Cocktail-HuBERT(14.76) > MT-HuBERT(13.93) > WavLM(13.83) > ConvTasNet(10.39)
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* Draft Paper( CN version almost done)
 
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第85行： 第95行：
 
|Yu Zhang
 
|Yu Zhang
 
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* GPU Util [https://z1et6d3xtb.feishu.cn/wiki/XX4NwX3tJiBDcgkMi0hcFUtInHh?from=from_copylink]
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* working on the big assignment and preparing for the final exam.
 
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第96行： 第107行：
 
|Junhui Chen
 
|Junhui Chen
 
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* prepare for final exam
 
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第107行： 第118行：
 
|Xiaolou Li
 
|Xiaolou Li
 
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* Prepared applications for 4 CDT programmes
 
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第129行： 第140行：
 
|Tianhao Wang
 
|Tianhao Wang
 
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* additional experiments for ChainSep [https://z1et6d3xtb.feishu.cn/docx/RG5Qd9ashoGhw5xc9M2cAVcHnLc]
 
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第140行： 第151行：
 
|Xiaoxue Luo
 
|Xiaoxue Luo
 
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* 2-5mix USS model for Huawei project
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* 2-5mix attractor-based USS model for Huawei project
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** SI-SDR : 2mix: 4.725    3mix: 0.866    4mix: -1.155    5mix: -2.709
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** SI-SDR : 2mix: 4.725，  3mix: 0.866，  4mix: -1.155，  5mix: -2.709
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* design a better solution for the Huawei project task
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* design a better solution for Huawei project
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** multi-head separation (two groups: speakers and events)
 
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第175行： 第187行：
 
|Bochao Hu
 
|Bochao Hu
 
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* P2S wer: 45%.data:20% GT 20% real seq 60% synthetic seq. llm can't correct with high per(> 30%)
 
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第186行： 第198行：
 
|Hongcheng Zhang
 
|Hongcheng Zhang
 
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* finish code of ASU-LLM (CLAP + HuBERT)
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* finish the main code of ASU-LLM (CLAP + HuBERT). The core architecture consists of a dual-stream encoder combining CLAP and Audio Hubert, integrated with LLaMA
 
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2026年1月5日 (一) 11:36的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • English version of the AI handbook - middle/high school almost done


Lantian Li
  • Final review of my MLA book (3/10)
  • MoE daily work
Wenqiang Du
  • Check AI course PPT(4/14)
  • Completed AI100 question task and will be uploaded to the platform this month
Yang Wei
  • Some additional result analysis and some revisions on manuscript according to the reviewer' comments. [1][2]
Ying Shi
Pengqi Li
  • Draft Paper: The framework and core content are complete.
  • AI Handbook: Checking and verifying references.
  • Winter Camp Preparation: Preparing materials for the Tsinghua University Middle School AI Winter Camp.
Junming Yuan
  • Aug-MT-HuBERT:
    • Fixed the pre-training to 100K steps, compared 3 noise ratios（50%, 80%, and 100%）and 2 different SNR sampling strategies([-3, 3] uniform / [0, 10] Gauss).
      • Best setting, PR performance(PER): 8.12 -> 8.08, ASR performance(WER): 9.22 -> 9.07
      • There is still around 3% gap compared to HuBERT.
  • SS adaptation(SI-SDRi):
    • Cocktail-HuBERT(14.76) > MT-HuBERT(13.93) > WavLM(13.83) > ConvTasNet(10.39)
  • Draft Paper( CN version almost done)
Yu Zhang
  • GPU Util [3]
  • working on the big assignment and preparing for the final exam.
Junhui Chen
  • prepare for final exam
Xiaolou Li
  • Prepared applications for 4 CDT programmes
Jiaying Wang
  • spk order separation training
Tianhao Wang
  • additional experiments for ChainSep [4]
Xiaoxue Luo
  • 2-5mix attractor-based USS model for Huawei project
    • SI-SDR : 2mix: 4.725， 3mix: 0.866， 4mix: -1.155， 5mix: -2.709
  • design a better solution for Huawei project
    • multi-head separation (two groups: speakers and events)
Yue Gu
  • write Phd defense slides
Lily
Bochao Hu
  • P2S wer: 45%.data:20% GT 20% real seq 60% synthetic seq. llm can't correct with high per(> 30%)
Hongcheng Zhang
  • finish the main code of ASU-LLM (CLAP + HuBERT). The core architecture consists of a dual-stream encoder combining CLAP and Audio Hubert, integrated with LLaMA
取自“http://index.cslt.org/mediawiki/index.php?title=2026-01-05&oldid=43799”