Neural Network Regression for Sound Source Localization Using Time Difference of Arrival Based on Parametric Homomorphic Deconvolution

This paper proposes a novel sound source localization system that combines parametric homomorphic deconvolution with neural network regression to estimate the angle of arrival from a single-channel signal. The system uses an analog adder to sum signals from three spatially arranged microphones, reducing system hardware complexity and requiring the estimation of time delays from a single-channel signal. Time delay features are extracted through parametric homomorphic deconvolution methods—Yule–Walker, Prony, and Steiglitz–McBride—and input to multilayer perceptrons configured with various structures. Simulations confirm that Steiglitz–McBride provides the sharpest and most accurate predictions with reduced model order, while Yule–Walker shows slightly better performance than Prony at higher orders. A hybrid learning strategy that combines synthetic and real-world data improves generalization and robustness across all angles. Experimental validations in an anechoic chamber support the simulation results, showing high correlation and low deviation values, especially with the Steiglitz–McBride method. The proposed sound source localization system demonstrates a compact and scalable design suitable for real-time and resource-constrained applications and provides a promising platform for future extensions in complex environments and broader signal interpretation domains.