Design of A Complete Algorithm With Mathematical Modeling For Sound Localization And Tracking Using Artificial Intelligence

Abstract

This research focuses on designing a novel acoustic source localization and tracking method that applies mathematical modeling in conjunction with machine learning (ML) and deep learning (DL) techniques to overcome issues arising from dynamic noise and reverberations characteristic of real-world environments. Conventional sound localization and tracking (SLT) techniques like Time Difference of Arrival (TDOA) and Beamforming have inherent drawbacks because they are passive techniques operating on basic geometric time delays, rendering them ineffective under dense, multi-source acoustic conditions. To resolve this, the proposed artificial intelligence (AI) enhanced system incorporates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformers to significantly boost localization precision and trajectory tracking efficacy. The system extracts dense spatial and temporal patterns from raw audio data, enabling real-time operation suitable for robotic, surveillance, and autonomous vehicle platforms. The experimental results reveal that this AI-based approach dramatically outperforms traditional frameworks across key metrics—including localization error, tracking mean squared error (MSE), average tracking drift, and system latency—at the expense of a manageable increase in computational complexity. Future work will investigate hardware-level optimizations to compress execution timelines and incorporate cross-modal sensor streams to reinforce system robustness in adverse climates.