Shichao Wu, Assistant Professor at Southwest University of Science and Technology

Shichao Wu is an Assistant Professor at the Southwest University of Science and Technology and a Researcher at the Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, School of Information Engineering. His primary research areas include acoustic computing and spatial perception intelligence. He has published a total of 26 academic papers, including 15 SCI journal papers, 10 top journal papers, and 1 highly cited paper. Previously, he obtained a B.E. from Southwest University of Science and Technology in 2017, M.S. from Northeastern University in 2020, and Ph.D. in Artificial Intelligence from Nankai University in 2024, under the supervision of Prof. Jingtai Liu.

吴仕超，西南科技大学特聘副教授，信息工程学院特殊环境机器人技术四川省重点实验室研究人员。 主要研究方向为声学计算、空间感知智能，已发表学术论文共26篇，其中SCI期刊论文15篇，TOP期刊论文10篇， 高被引论文1篇。在此之前，他于2017年在西南科技大学取得学士学位，2020年在东北大学取得硕士学位， 2024年在南开大学取得博士学位（人工智能专业，导师：刘景泰教授）。

Email  /  Google Scholar  /  CSDN  /  Github

• December 2023: Published one journal research paper concerning acoustic footstep-based person identification and localization (dataset & method) in Information Fusion.
• August 2023: Published one journal research paper concerning one audio augmentation approach (HAAC) for sound event localization and detection (SELD) in Applied Acoustics.
• January 2023: Published one journal research paper concerning acoustic footstep based person identification (dataset & method) in Knowledge-Based Systems.
• August 2022: Published one journal research paper concerning context-aware emotion recognition in IEEE Transactions on Neural Networks and Learning Systems.

I’m currently engaged in and plan to research spatial perception intelligence for robotics. These works primarily focus on (I) sound event localization and detection; (II) automatic 3D reconstruction; and (III) spatial perception intelligence-enabled dexterous manipulation and embodied navigation. These works tried to perceive and understand the spatial characteristics of humans and objects in the environment from both visual and auditory perspectives, thereby enabling Generalist Robots to navigate and operate effectively in various complex scenarios.

My publications can be found here. See also: Google Scholar. Here are some recent publications. Representative papers are highlighted.

We build the acoustic footstep-based person identification and localization dataset (AFPILD) by unifying the identify and locate tasks for the first time, concerning the clothing and shoe type covariates.

We propose one hierarchical audio augmentation chain (HAAC) that contains feature map augmentation, audio channel swapping, and sample mixup to augment the audio features for the SELD task.

We propose one improved acoustic footstep-based person identification dataset (AFPID-II) from 41 subjects, counting over 14 h of footstep audios.

We propose the hierarchical contexts (the entity context, the global context, and the scene context) based emotion recognition method with scene graphs.

Propose one driving fatigue detection approach based on the brain functional network, and provide one way for the key EEG electrodes and rhythms selection.

Propose the EFDMs based on EEG with STFT, and combine them with the deep convolutional neural network for emotion recognition.

Propose one driving fatigue detection method based on multiple nonlinear features fused with multiple kernel learning (MKL) based SVM.

Propose one fatigue detection approch with transfer learning based on the Electrode-Frequency Distribution Maps (EFDMs) of EEG signals.

"Highly Cited Paper of 2019" in the Journal of Electronics & Information Technology

Reviewer:

IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

Engineering Applications of Artificial Intelligence (EAAI)

International Journal of Intelligent Systems

Applied Intelligence

IROS 2023

Special thanks to Jon Barron for the website's source code

Last updated: Jul. 2024