23 citations · 24 across the 5 of their papers we have counts for
5 papers
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…
Lightweight Speaker Verification Using Transformation Module with Feature Partition and Fusion
Yanxiong Li, Zhongjie Jiang, Qisheng Huang +2
Although many efforts have been made on decreasing the model complexity for speaker verification, it is still challenging to deploy speaker verification systems with satisfactory r…
Few-shot Class-incremental Audio Classification Using Stochastic Classifier
Yanxiong Li, Wenchang Cao, Jialong Li +2
It is generally assumed that number of classes is fixed in current audio classification methods, and the model can recognize pregiven classes only. When new classes emerge, the mod…
Few-shot Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes
Yanxiong Li, Wenchang Cao, Wei Xie +2
Most existing methods for audio classification assume that the vocabulary of audio classes to be classified is fixed. When novel (unseen) audio classes appear, audio classification…