“2026-04-20”版本间的差异

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第6行: 第6行:
 
|Dong Wang
 
|Dong Wang
 
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* Check 2n semester textbook (done)
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* Refine ai textbook college version (1/2)
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* Check paper for micro-magenetics
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第28行: 第31行:
 
|Wenqiang Du
 
|Wenqiang Du
 
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* Spech separation task
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** Reproduce the SpatialNet method (2 spk mix)
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** Train with mixed speakers: 2spk, 3spk, and 4spk,average SI-SDR up 7 (small data,incompelet separation)
 
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第39行: 第44行:
 
|Yang Wei
 
|Yang Wei
 
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* Audio separation model training with (L1 loss + SI-SDR loss) for low loudness problem
 
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第86行: 第91行:
 
|Pengqi Li
 
|Pengqi Li
 
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* Core Work: Extended the findings of the ICASSP 2024 paper to the Chinese-language scenario
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* Progress Update: Drafted the paper for submission to Journal of Chinese Information Processing; the draft has been reviewed by Prof.Wang and is currently under revision.
 
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第146行: 第152行:
 
|Bochao Hu
 
|Bochao Hu
 
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* Topics-based VSR-LLM (100h topics data for now)
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** baseline: same encoder(FT) + transformer cer: 0.396 + Qwen-7B cer: 0.345 + Qwen-VL-7B cer: 0.318
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** + peripheral info is in training
 
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第157行: 第165行:
 
|Hongcheng Zhang
 
|Hongcheng Zhang
 
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*test qwen-omni model with different prompt
 
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2026年4月20日 (一) 11:01的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • Check 2n semester textbook (done)
  • Refine ai textbook college version (1/2)
  • Check paper for micro-magenetics
Lantian Li
Wenqiang Du
  • Spech separation task
    • Reproduce the SpatialNet method (2 spk mix)
    • Train with mixed speakers: 2spk, 3spk, and 4spk,average SI-SDR up 7 (small data,incompelet separation)
Yang Wei
  • Audio separation model training with (L1 loss + SI-SDR loss) for low loudness problem
Ying Shi
Yue Gu
  • write my Phd thesis(70% in progress)
Lily
  • Question bank for three AI Literacy books
  • Lecturer Management System released
  • AIGE Center annual report PPT
  • AIGE routine work
Pengqi Li
  • Core Work: Extended the findings of the ICASSP 2024 paper to the Chinese-language scenario
  • Progress Update: Drafted the paper for submission to Journal of Chinese Information Processing; the draft has been reviewed by Prof.Wang and is currently under revision.
Junming Yuan
  • ZH paper refinement(done)
Yu Zhang
Junhui Chen
  • get bad sick
  • paper refinement for several versions; references finding
Xiaoxue Luo
  • attractor-based USS
    • attractor counting accuracy of the 2-3mix model is still lower than expected
    • contact the author of the paper, will retrain based on his advice
Bochao Hu
  • Topics-based VSR-LLM (100h topics data for now)
    • baseline: same encoder(FT) + transformer cer: 0.396 + Qwen-7B cer: 0.345 + Qwen-VL-7B cer: 0.318
    • + peripheral info is in training
Hongcheng Zhang
  • test qwen-omni model with different prompt
Weiman Sun
  • Write my graduation thesis
  • Test the Qwen-omni models
Ge Gao
Shuailong Li
  • Reproduction on the MTASS original dataset(loss:MSE+SNR)
    • SDRi(speech=11.94 music=10.80 others=8.97 avg=10.57)
    • The perceived volume has not decreased
  • Reproduction on the MTASS original dataset(loss:MSE+SI-SDR)
    • Some problems have occurred and are being resolved