“2022-01-17”版本间的差异

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|Haoran Sun
 
|Haoran Sun
 
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* experiments for rebuttal
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* update CycleFlow code
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* AutoVC with cycle loss
 
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* More training and evaluation on AutoVC
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* More attempts on cycle loss
 
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|Chen Chen
 
|Chen Chen
 
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* Figure out the influence of symbol & feature
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* Test w2v2.0 feats with LSTM in a supervised way
 
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* Test the influence of feature & preprocessing & training epochs
 
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|Zixi Yan
 
|Zixi Yan
 
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* Write code for extracting wav2vec features in Kaldi
 
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|Haoyu Jiang
 
|Haoyu Jiang
 
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*Complete the data partition
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*Prepare group report
 
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2022年1月17日 (一) 10:59的最后版本

People This Week Next Week Task Tracking (DeadLine)
Dong Wang
  • OLR workshop preparation
  • CNSRC plan: metric design
  • Capsulate review: emotion recognition
  • Visual-audio SID experiment plan design
  • Keep on multi-modal review & information fusion
Yunqi Cai
  • Low-light image simulation and data preparation
  • capsule network review
  • Research plan guidance for interns
  • Conceptual model of intelligent sensor
Lantian Li
  • Polish CNCSRC plan
  • Write hard trials paper
  • Finish hard trials paper
Ying Shi
  • Analysis FA of Speech engrave
  • Speech engrave with position embedding here
  • Analysis Speech engrave with position embedding
Haoran Sun
  • experiments for rebuttal
  • update CycleFlow code
  • AutoVC with cycle loss
  • More training and evaluation on AutoVC
  • More attempts on cycle loss
Chen Chen
  • Figure out the influence of symbol & feature
  • Test w2v2.0 feats with LSTM in a supervised way
  • Test the influence of feature & preprocessing & training epochs
Pengqi Li
  • Read Paper (RELAX,RISE); Finish Demo about RISE
  • Evaluate the importance maps with Deletion and Insertion
  • Test MFCC
  • Finish RELAX
Weida Liang
  • Paper sharing on any-to-any VC
  • Finish training wav2vec model
  • Ongoing wav2vec+AE model
  • Finish all model training
  • Test and comparison analysis
Zixi Yan
  • Write code for extracting wav2vec features in Kaldi
Sirui Li
  • Extract MFCC features for GAN
  • Write a project application
  • Go on GAN experiment
Haoyu Jiang
  • Complete the data partition
  • Prepare group report
Ruihai Hou
  • Read the UIS-RNN paper
  • Run some tests on speaker diarization
  • Try to train the UIS-RNN model
Renmiao Chen
  • Do three experiments for PMI.
  • Do something for programming TA.
  • Finish experiments for PMI.
取自“http://cslt.org/mediawiki/index.php?title=2022-01-17&oldid=37660”