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| 第70行： |
第70行： |
| | |Xiaolou Li | | |Xiaolou Li |
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| − | * | + | * Mid-term framework and report |
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| 第81行： |
第81行： |
| | |Zehua Liu | | |Zehua Liu |
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| − | * | + | *Mid-term framework and report |
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| 第106行： |
第106行： |
| | |Wan Lin | | |Wan Lin |
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| − | * | + | * Phd application |
| | + | * MD task |
| | + | ** Reproduce previous framework in shiyin's code: work normally |
| | + | ** Prepare for similar phoneme pronunciation replacement |
| | + | * NC's report |
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| 第128行： |
第132行： |
| | |Xiaoxue Luo | | |Xiaoxue Luo |
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| − | * | + | * train of attractor-based speech separation |
| | + | ** the training loss is normal, but the training is too slow. I plan to change the current code from Chainer version to PyTorch version |
| | + | * Proposal framework and report |
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| 第153行： |
第159行： |
| | |Jiaying Wang | | |Jiaying Wang |
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| − | * | + | * A Loudness-Perceptual-Biased Speech Separation Method paper modification |
| | + | * mid-term report |
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| | * | | * |
| 第193行： |
第200行： |
| | |Yang Wei | | |Yang Wei |
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| − | * | + | * Reproduce shiying's experiment on LibriSpeech (train/test: random data aug; ROC AUC 0.99) |
| | + | * Training with other data aug method: replace a word with similar pronunciation from lexicon. (under training) |
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| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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- AI handbook for primary schools, 4th grad.
- Talk in middle school of Renmin Middle School.
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| Lantian Li
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- Review AI Book – College Edition (52/53).
- Organize teaching course materials.
- Guide BUPT-STU's proposal and mid-term reports.
- Project: HUAWEI Phase I Delivery Report and FENYINTA AVSD Schedule
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- Proofread my MLA Book.
- Review AI Book – High School Edition.
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| Ying Shi
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| Zhenghai You
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| Junming Yuan
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- check the resources for the middle-school AI course(16/20)
- paper reading
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| Xiaolou Li
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- Mid-term framework and report
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| Zehua Liu
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- Mid-term framework and report
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| Pengqi Li
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- Conduct supplementary experiments and write the paper
- AI course of Tsinghua University Middle School
- Review the practical manual(v2) again（Liuzhou high school and Xian Gongshang）. I think is good enough.
- Check the PPT(5/10)
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|
| Wan Lin
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- Phd application
- MD task
- Reproduce previous framework in shiyin's code: work normally
- Prepare for similar phoneme pronunciation replacement
- NC's report
|
|
|
| Tianhao Wang
|
- mid-term framework & report
|
|
|
| Xiaoxue Luo
|
- train of attractor-based speech separation
- the training loss is normal, but the training is too slow. I plan to change the current code from Chainer version to PyTorch version
- Proposal framework and report
|
|
|
| Junhui Chen
|
- LLM:
- finish Baseline & Reflexion metric collection (without analysis yet)
- continue reading papers from COLM & NIPS 2025 (9/26)
- proposal discussing with @ZhangYu
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|
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| Jiaying Wang
|
- A Loudness-Perceptual-Biased Speech Separation Method paper modification
- mid-term report
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|
|
| Yu Zhang
|
- AED: add provided VPU hard case for training
- With a 2-second window length during training and testing, "WYL-type cough" can be effectively solved;
- With a 0.6-second window length, adding new training data yields only a minor improvement.
- LLM:
- finish Baseline & Reflexion metric collection (without analysis yet)
- proposal discussing with @chenjunhui
- AI2S webset deploy
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| Wenqiang Du
|
- Check PPT for primary/middle/high school
- Writing project documents with Weiman Sun
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| Yang Wei
|
- Reproduce shiying's experiment on LibriSpeech (train/test: random data aug; ROC AUC 0.99)
- Training with other data aug method: replace a word with similar pronunciation from lexicon. (under training)
|
|
|
| Yue Gu
|
- job hunting and Phd thesis
- Mispronunciation task: 1 use ChatGPT to generate the negtive samples with similar pronunciation, 2 compute the similar matrix between speech embeddings and phone embeddings, then conduct dynamic programming algorithm on it to find the maximum cumulative-similarity path. The mispronunciations will corresponding to a lower similar scores.
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| Qi Qu
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- Debugging for elderly alarm customization: possible heap corruption.
- Experiments w/ RKNN toolkit.
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| Lily
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- Paper reading
- Check AI textbook 2nd semester, primary school version (3 grad)
- AIGE & AIRadiance dailywork
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