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| 第59行： |
第59行： |
| | |Haoran Sun | | |Haoran Sun |
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| − | * | + | * some tests on our model |
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| − | * | + | * make some more efficient attempts |
| | + | * ——remove rhythm and pitch encoders |
| | + | * ——increase distance between speakers |
| | + | * ——improve content encoder |
| | + | * ——make use of speaker label |
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| | * | | * |
| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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- Refine spoof paper
- Prepare talk for information theory in NN
- Prepare talk for representation investigation.
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| 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
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| Lantian Li
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- Finish course on AI.
- Study speaker separation and think about structural embedding.
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- Finish ETM response.
- Exps of hard trials.
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| Ying Shi
|
- Report about e2e kws
- speech engrave (garbage node, sil training data, text to speech attention)
- analyse fenyinta test data
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- more analyse about speech engrave(speech to text attention)
- speech engrave (text to speech attention)
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| Haoran Sun
|
|
- make some more efficient attempts
- ——remove rhythm and pitch encoders
- ——increase distance between speakers
- ——improve content encoder
- ——make use of speaker label
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|
| Chen Chen
|
- 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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| Pengqi Li
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- reproduce a series of CAM method on speaker classification
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| Qingyang Zhu
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| Weida Liang
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- Finish the first version on improved exemplar autoencoder with cycle loss
- Rethink the theory analysis part
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- Test on never-before-seen speaker conversion
- Review the code of wav2vec, StarGAN and PPG based GAN
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| Zixi Yan
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| Sirui Li
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| Haoyu Jiang
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- Face sampling in CNCeleb dataset
- Filter videos without the target's face
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| Ruihai Hou
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| Renmiao Chen
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