23 citations · 43 across the 9 of their papers we have counts for
10 papers
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…
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…
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…
Speaker verification using attentive multi-scale convolutional recurrent network
Yanxiong Li, Zhongjie Jiang, Wenchang Cao +1
In this paper, we propose a speaker verification method by an Attentive Multi-scale Convolutional Recurrent Network (AMCRN). The proposed AMCRN can acquire both local spatial infor…
Few-Shot Speaker Identification Using Lightweight Prototypical Network with Feature Grouping and Interaction
Yanxiong Li, Hao Chen, Wenchang Cao +2
Existing methods for few-shot speaker identification (FSSI) obtain high accuracy, but their computational complexities and model sizes need to be reduced for lightweight applicatio…
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…
Few-shot Class-incremental Audio Classification Using Adaptively-refined Prototypes
Wei Xie, Yanxiong Li, Qianhua He +2
New classes of sounds constantly emerge with a few samples, making it challenging for models to adapt to dynamic acoustic environments. This challenge motivates us to address the n…
Domestic Activity Clustering from Audio via Depthwise Separable Convolutional Autoencoder Network
Yanxiong Li, Wenchang Cao, Konstantinos Drossos +1
Automatic estimation of domestic activities from audio can be used to solve many problems, such as reducing the labor cost for nursing the elderly people. This study focuses on sol…
Few-Shot Speaker Identification Using Depthwise Separable Convolutional Network with Channel Attention
Yanxiong Li, Wucheng Wang, Hao Chen +3
Although few-shot learning has attracted much attention from the fields of image and audio classification, few efforts have been made on few-shot speaker identification. In the tas…