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

Abstract

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.