Disong Wang
Papers
1
Total Citations
7
H-Index
1
About
Disong Wang is a researcher whose work lies at the intersection of acoustic signal processing and machine learning, with a particular focus on robust sound source localization. His key contributions center on Direction of Arrival (DOA) estimation, especially for small microphone arrays used in challenging real-world environments like service robotics and smart homes. Wang’s most cited paper, “Learning a robust DOA estimation model with acoustic vector sensor cues” (2017, 7 citations), addresses a critical limitation of classic non-learning methods, which often fail under low signal-to-noise ratio or high reverberation conditions. By integrating acoustic vector sensor cues into a learning-based framework, he demonstrated how neural models can achieve far greater accuracy and robustness in adverse acoustic settings. This work has been influential in advancing practical DOA estimation for compact, low-cost sensor arrays. Wang’s research is notable for bridging the gap between traditional signal processing and modern deep learning, offering scalable solutions for real-time auditory scene analysis. His contributions are particularly relevant for applications in human-robot interaction and smart environments, where reliable sound source tracking is essential.
Research Focus
Key Achievements
Top Papers
- 1Learning a robust DOA estimation model with acoustic vector sensor cues7 citations · 2017