activity
20222024
most citedImproving EEG-based Emotion Recognition by Fusing Time-frequency And Spatial Representations

1 citations · 3 across the 9 of their papers we have counts for

collaborators

6 papers

cs.LG2023

Two-stage Denoising Diffusion Model for Source Localization in Graph Inverse Problems

Bosong Huang, Weihao Yu, Ruzhong Xie +2

Source localization is the inverse problem of graph information dissemination and has broad practical applications. However, the inherent intricacy and uncertainty in information d…

eess.SP20231 cited

Improving EEG-based Emotion Recognition by Fusing Time-frequency And Spatial Representations

Kexin Zhu, Xulong Zhang, Jianzong Wang +2

Using deep learning methods to classify EEG signals can accurately identify people's emotions. However, existing studies have rarely considered the application of the information i…

cs.SD20231 cited

Feature-Rich Audio Model Inversion for Data-Free Knowledge Distillation Towards General Sound Classification

Zuheng Kang, Yayun He, Jianzong Wang +3

Data-Free Knowledge Distillation (DFKD) has recently attracted growing attention in the academic community, especially with major breakthroughs in computer vision. Despite promisin…

cs.SD20231 cited

Improving Music Genre Classification from Multi-Modal Properties of Music and Genre Correlations Perspective

Ganghui Ru, Xulong Zhang, Jianzong Wang +2

Music genre classification has been widely studied in past few years for its various applications in music information retrieval. Previous works tend to perform unsatisfactorily, s…

cs.SD2022

Tiny-Sepformer: A Tiny Time-Domain Transformer Network for Speech Separation

Jian Luo, Jianzong Wang, Ning Cheng +3

Time-domain Transformer neural networks have proven their superiority in speech separation tasks. However, these models usually have a large number of network parameters, thus ofte…

cs.SD2022

Uncertainty Calibration for Deep Audio Classifiers

Tong Ye, Shijing Si, Jianzong Wang +2

Although deep Neural Networks (DNNs) have achieved tremendous success in audio classification tasks, their uncertainty calibration are still under-explored. A well-calibrated model…