Shardul Yadav

Papers

Total Citations

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About

Shardul Yadav is an emerging researcher in acoustic signal processing and machine learning, with a focused interest in direction-of-arrival (DOA) estimation for acoustic sources. His most cited work, "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 estimate the direction of sound sources using signals from uniform arrays. This contribution is particularly significant for applications in surveillance, robotics, and defense, where accurate localization is critical. By training an SVR model on acoustic signals, Yadav’s method offers a robust alternative to traditional techniques, enhancing precision in noisy environments. While his citation count is modest, reflecting an early-career stage, the work demonstrates a clear impact on practical, real-world systems. Yadav’s research bridges machine learning and acoustics, positioning him as a promising voice in sensor-based localization. His achievements highlight a commitment to advancing intelligent signal processing, with potential for further contributions to autonomous systems and security technologies.