The goal of LocaGen is to improve the localization performance of audio signals in the 2-D beam localization problem. LocaGen reduces sampling quantization errors through machine learning models trained on realistic synthetic data generated by a simulation. The system increases the accuracy of both direction-of-arrival (DOA) and precise location estimation of an audio beam from an array of three microphones. We demonstrate LocaGen's efficacy on a low-powered embedded system with an increased localization accuracy with a minimal increase in real-time resource usage. LocaGen was demonstrated to reduce DOA error by approximately 67% even with a microphone array of only 10 kHz in audio processing.
@article{arxiv.2512.07872,
title = {LocaGen: Sub-Sample Time-Delay Learning for Beam Localization},
author = {Ishaan Kunwar and Henry Cantor and Tyler Rizzo and Ayaan Qayyum},
journal= {arXiv preprint arXiv:2512.07872},
year = {2025}
}