“2021-12-13”版本间的差异

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（以“{| class="wikitable" !People !! This Week !! Next Week !! Task Tracking (<font color="red">DeadLine</font>) |- |- |Dong Wang || * || * || * |- |- |Yunqi Cai |...”为内容创建页面）
 
 
（12位用户的17个中间修订版本未显示）
第5行： 第5行：
 
|Dong Wang
 
|Dong Wang
 
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* Spoof paper refined
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* Start the hard trials paper
 
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* Hard trials paper
 
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第16行： 第17行：
 
|Yunqi Cai
 
|Yunqi Cai
 
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* img fusion network construction
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* infra experiments plan for interns
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* bayesian optimization paper review
 
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第27行： 第30行：
 
|Lantian Li
 
|Lantian Li
 
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* Refine AI course v2.
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* Check spoof paper.
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* Finish my defences.
 
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* Finish ETM response.
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* Exps of hard trials.
 
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第38行： 第44行：
 
|Ying Shi
 
|Ying Shi
 
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* Test fncmd and speech engrave on huawei_cross_channel data [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/8/82/Speech_engrave_fncmd_huawei_cross.png here]
 
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*  
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* Retrain speech engrave model(make speech engrave and fncmd are Comparable on far field test set)
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** Huawei cross channel data
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** Score margin
 +
** Discriminative training
 +
* Retrain fncmd model with huawei data.
 
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第49行： 第59行：
 
|Haoran Sun
 
|Haoran Sun
 
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* some analysis on c-vector
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* training processing of c-vector
 
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* remove f0 decoder of c-vector
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* a easier model with only content and speaker encoders based on long-short term assumption
 
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第60行： 第72行：
 
|Chen Chen
 
|Chen Chen
 
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* perform kmeans and pca on wav2vec result
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* check GAN
 
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* fix bug of uasr_model
 
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第71行： 第84行：
 
|Pengqi Li
 
|Pengqi Li
 
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* Verifying the correctness of the a series of cam method
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* reproduce the method of Layer-CAM on classification
 
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* more experiment and analysis on this method
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|Qingyang Zhu
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第92行： 第96行：
 
|Weida Liang
 
|Weida Liang
 
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* Finish training for not-ever-seen speaker on baseline AE and cycle model
 +
* Build the framework of wav2vec model
 
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*  
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* Full test on baseline & cycle model
 +
* More details need to be discussed on wav2vec model
 
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第102行： 第108行：
 
|Zixi Yan
 
|Zixi Yan
 
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* Fine-tune the wav2vec model on dev-other
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* Test the effect of Tibetan adjusted model
 
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第112行： 第119行：
 
|Sirui Li
 
|Sirui Li
 
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* Compare the effects of TIMIT and Tibetan fine-tune
 
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* More comparative experiments
 
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第123行： 第130行：
 
|Haoyu Jiang
 
|Haoyu Jiang
 
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* Resampling the data
 
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* Set thresholds to divide data
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* Check the sampled images
 
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|Ruihai Hou
 
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|Renmiao Chen
 
|Renmiao Chen
 
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*  
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* choose thresholds for dividing high-confident data, mid-confident data, low-confident data.
 +
* check the thresholds.
 +
* use speechbrain to do IDR task.
 
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*  
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* do more task with the data.
 +
* finish the report.
 
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*   
 
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2021年12月13日 (一) 11:24的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • Spoof paper refined
  • Start the hard trials paper
  • Hard trials paper
Yunqi Cai
  • img fusion network construction
  • infra experiments plan for interns
  • bayesian optimization paper review
Lantian Li
  • Refine AI course v2.
  • Check spoof paper.
  • Finish my defences.
  • Finish ETM response.
  • Exps of hard trials.
Ying Shi
  • Test fncmd and speech engrave on huawei_cross_channel data here
  • Retrain speech engrave model(make speech engrave and fncmd are Comparable on far field test set)
    • Huawei cross channel data
    • Score margin
    • Discriminative training
  • Retrain fncmd model with huawei data.
Haoran Sun
  • some analysis on c-vector
  • training processing of c-vector
  • remove f0 decoder of c-vector
  • a easier model with only content and speaker encoders based on long-short term assumption
Chen Chen
  • perform kmeans and pca on wav2vec result
  • check GAN
  • fix bug of uasr_model
Pengqi Li
  • Verifying the correctness of the a series of cam method
  • reproduce the method of Layer-CAM on classification
  • more experiment and analysis on this method
Weida Liang
  • Finish training for not-ever-seen speaker on baseline AE and cycle model
  • Build the framework of wav2vec model
  • Full test on baseline & cycle model
  • More details need to be discussed on wav2vec model
Zixi Yan
  • Fine-tune the wav2vec model on dev-other
  • Test the effect of Tibetan adjusted model
Sirui Li
  • Compare the effects of TIMIT and Tibetan fine-tune
  • More comparative experiments
Haoyu Jiang
  • Resampling the data
  • Set thresholds to divide data
  • Check the sampled images
Renmiao Chen
  • choose thresholds for dividing high-confident data, mid-confident data, low-confident data.
  • check the thresholds.
  • use speechbrain to do IDR task.
  • do more task with the data.
  • finish the report.
取自“http://cslt.org/mediawiki/index.php?title=2021-12-13&oldid=37435”