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Few-Shot Class-Incremental Audio Classification Using Pseudo-Incrementally Trained Embedding Learner and Continually Updated Stochastic Classifier
Yanxiong Li, Wenchang Cao, Jiaxin Tan +2
Few-shot Class-incremental Audio Classification (FCAC) aims to progressively recognize incremental classes with few tagged samples and meanwhile memorize base classes. To achieve s…
Few-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes
Yanxiong Li, Jiaxin Tan, Qianqian Li +3
Most existing audio classification methods suppose that each query (testing) sample belongs to a class of support (training) samples, and misrecognize samples of unseen classes as…
Few-shot Class-variable Incremental Audio Classification via Prototype Adaptation and Pseudo Class-variable Training
Yanxiong Li, Guoqing Chen, Qianqian Li +1
In the task of few-shot class-incremental audio classification, the number of classes is assumed to always increase without considering the possibility of decrease. However, the nu…
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…