1 citations · 1 across the 4 of their papers we have counts for
4 papers
Cross Domain Few-Shot Class-Incremental Audio Classification Via Adversarial Contrastive Learning
Yongjie Si, Yanxiong Li, Sen Huang +1
Current Few-shot Class-incremental Audio Classification (FCAC) methods assume that samples of base and incremental classes are in the same domain (following the same distribution).…
Fully Few-shot Class-incremental Audio Classification Using Multi-level Embedding Extractor and Ridge Regression Classifier
Yongjie Si, Yanxiong Li, Jiaxin Tan +2
In the task of Few-shot Class-incremental Audio Classification (FCAC), training samples of each base class are required to be abundant to train model. However, it is not easy to co…
Fully Few-shot Class-incremental Audio Classification Using Expandable Dual-embedding Extractor
Yongjie Si, Yanxiong Li, Jialong Li +2
It's assumed that training data is sufficient in base session of few-shot class-incremental audio classification. However, it's difficult to collect abundant samples for model trai…
Low-Complexity Acoustic Scene Classification Using Parallel Attention-Convolution Network
Yanxiong Li, Jiaxin Tan, Guoqing Chen +3
This work is an improved system that we submitted to task 1 of DCASE2023 challenge. We propose a method of low-complexity acoustic scene classification by a parallel attention-conv…