Papers
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Total Citations
38
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About
Samta Joshi is a researcher whose work lies at the intersection of signal processing and environmental acoustics. Her primary focus is on developing innovative methods for environmental sound classification, a field critical for applications ranging from wildlife monitoring to urban noise management. Joshi’s most notable contribution is her 2019 paper, "Environmental sound classification using optimum allocation sampling based empirical mode decomposition," which has garnered 38 citations. This work introduces a novel approach that enhances the accuracy and efficiency of classifying non-stationary environmental sounds by optimizing the sampling process within empirical mode decomposition. By addressing the challenges of real-world acoustic data, Joshi’s method offers a robust framework for distinguishing between diverse sound sources, such as animal calls, traffic noise, and industrial sounds. Her research has practical implications for smart city initiatives and ecological surveillance, demonstrating how advanced signal processing can bridge the gap between raw acoustic data and actionable insights. With a growing citation record, Joshi is establishing herself as a thoughtful contributor to the field, combining theoretical rigor with applied problem-solving. Her work continues to inspire students and researchers interested in the intersection of machine learning, acoustics, and environmental science.
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Top Papers
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