18 citations · 21 across the 9 of their papers we have counts for
10 papers
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…
Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration
Yanxiong Li, Mingle Liu, Wucheng Wang +2
Recent efforts have been made on acoustic scene classification in the audio signal processing community. In contrast, few studies have been conducted on acoustic scene clustering,…
Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet
Yanxiong Li, Wenchang Cao, Wei Xie +3
We present a work on low-complexity acoustic scene classification (ASC) with multiple devices, namely the subtask A of Task 1 of the DCASE2021 challenge. This subtask focuses on cl…
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…