Mohammed Usman
Papers
1
Total Citations
7
H-Index
1
About
Mohammed Usman is a researcher whose work sits at the intersection of machine learning and acoustic signal processing, with a particular focus on direction-of-arrival (DOA) estimation. His most cited paper, "Support Vector Regression based Direction of Arrival Estimation of an Acoustic Source" (2020, 7 citations), introduces a novel approach that leverages support vector regression (SVR) to accurately pinpoint the location of sound sources. This work is instrumental for applications ranging from surveillance and robotics to defense, offering a robust alternative to traditional DOA methods by training a machine-learning model on signals from uniform arrays. Usman’s contribution lies in bridging the gap between classical acoustics and modern AI, demonstrating how regression techniques can enhance precision in real-world environments. With his research gaining traction among peers, his work is paving the way for smarter, more adaptive acoustic sensing systems. For students and researchers exploring the fusion of signal processing and machine learning, Usman’s studies provide a compelling case for the power of data-driven approaches in solving complex spatial problems.
Research Focus
Key Achievements
Top Papers
- 1