“2021-12-06”版本间的差异

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|Lantian Li
 
|Lantian Li
 
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* Finish course on AI.
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* Study speaker separation and think about structural embedding.
 
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* Finish ETM response.
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* Exps of hard trials.
 
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|Ying Shi
 
|Ying Shi
 
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* Report about e2e kws
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* speech engrave (garbage node, sil training data, text to speech attention)
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* analyse fenyinta test data [[http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=shiying&step=view_request&cvssid=829 here]]
 
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* more analyse about speech engrave(speech to text attention)
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* speech engrave (text to speech attention)
 
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|Haoran Sun
 
|Haoran Sun
 
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* some tests on our model
 
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* make some more efficient attempts
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* ——remove rhythm and pitch encoders
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* ——increase distance between speakers
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* ——improve content encoder
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* ——make use of speaker label
 
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|Chen Chen
 
|Chen Chen
 
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* pre-process audio data & train GAN with wav2vec2 output data directly
 
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* use kmeans and pca clustering wav2vec2 output to build better segment representation
 
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|Zixi Yan
 
|Zixi Yan
 
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* Training wav2vec model
 
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|Sirui Li
 
|Sirui Li
 
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* Fine-tune the wav2vec model
 
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* Comparing Tibetan and Chinese fine-tune results
 
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|Haoyu Jiang
 
|Haoyu Jiang
 
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* Face sampling in CNCeleb dataset
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* Filter videos without the target's face
 
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|Ruihai Hou
 
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|Renmiao Chen
 
|Renmiao Chen
 
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* Sample some audio,listen and analyze
 
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* divide data
 
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2021年12月13日 (一) 10:57的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • Refine spoof paper
  • Prepare talk for information theory in NN
  • Prepare talk for representation investigation.
  • Finish poof paper
Yunqi Cai
  • review papers about CQDs
  • Verify the deconvolution of infrared and visible faces
  • Verify infrared and visible image fusion based on GLOW model
  • Arrange research plans for interns
Lantian Li
  • Finish course on AI.
  • Study speaker separation and think about structural embedding.
  • Finish ETM response.
  • Exps of hard trials.
Ying Shi
  • Report about e2e kws
  • speech engrave (garbage node, sil training data, text to speech attention)
  • analyse fenyinta test data [here]
  • more analyse about speech engrave(speech to text attention)
  • speech engrave (text to speech attention)
Haoran Sun
  • some tests on our model
  • make some more efficient attempts
  • ——remove rhythm and pitch encoders
  • ——increase distance between speakers
  • ——improve content encoder
  • ——make use of speaker label
Chen Chen
  • pre-process audio data & train GAN with wav2vec2 output data directly
  • use kmeans and pca clustering wav2vec2 output to build better segment representation
Pengqi Li
  • reproduce a series of CAM method on speaker classification
Qingyang Zhu
Weida Liang
  • Finish the first version on improved exemplar autoencoder with cycle loss
  • Rethink the theory analysis part
  • Test on never-before-seen speaker conversion
  • Review the code of wav2vec, StarGAN and PPG based GAN
Zixi Yan
  • Training wav2vec model
Sirui Li
  • Fine-tune the wav2vec model
  • Comparing Tibetan and Chinese fine-tune results
Haoyu Jiang
  • Face sampling in CNCeleb dataset
  • Filter videos without the target's face
Renmiao Chen
  • Sample some audio,listen and analyze
  • divide data
取自“http://index.cslt.org/mediawiki/index.php?title=2021-12-06&oldid=37434”