Ying Tong
Papers
1
Total Citations
3
H-Index
1
About
Ying Tong is a researcher whose work centers on acoustic signal processing, with a particular focus on microphone array-based sound source localization (SSL)—a critical technology for applications ranging from video conferencing and robotic hearing to speech enhancement and recognition. Her major contribution lies in advancing SSL through deep learning, specifically by developing a convolutional residual network that outperforms traditional methods in challenging noisy and reverberant environments. This work, published in 2022 and garnering 3 citations, addresses a long-standing limitation of conventional SSL techniques, which often falter under adverse acoustic conditions. By integrating residual learning with convolutional architectures, Tong’s approach enhances the robustness and accuracy of sound source detection, offering practical improvements for real-world systems like smart assistants and autonomous robots. Her research bridges the gap between theoretical signal processing and applied machine learning, making her a notable figure in the field of audio AI. For students and researchers, Tong’s work exemplifies how deep learning can solve persistent problems in acoustic sensing, paving the way for more reliable human-machine interaction in noisy environments.
Research Focus
Key Achievements
Top Papers
- 1