[DCASE 2022 tech. report] The technical report for our submitted system in the DCASE 2022 challenge, task 4. We integrate the pretrained ATST-Clip with a CRNN model and obtain 4th place in the challenge (single model + using extra dataset).
Nian Shao; Xian Li; Xiaofei Li
[Accepcted by Interspeech 2022] We introduce a semi-supervised learning method for SED and test its performance over DESED dataset. We obtain SOTA performance in all CRNN-based models, surpassing SCT and ICT methods.
Nian Shao; Erfan Loweimi; Xiaofei Li
[Accepted by KBS] We propose a novel solution for solving the representation bottleneck. By theoratically provements, we verify the existence of the problem and successfully increase the representation ability of the Transformer model. Experiments are done with language modeling/GLUE tasks on Transformer/BERT models.
Zhong Zhang; Nian Shao; Chongming Gao; Rui Miao; Qinli Yang; Junming Shao
