{"ID":2835719,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.07872","arxiv_id":"2512.07872","title":"LocaGen: Sub-Sample Time-Delay Learning for Beam Localization","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2512.07872","url_pdf":"https://arxiv.org/pdf/2512.07872v1","authors":"[\"Ishaan Kunwar\",\"Henry Cantor\",\"Tyler Rizzo\",\"Ayaan Qayyum\"]","published":"2025-11-27T01:39:58Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"eess.AS\",\"eess.SP\"]","methods":"[]","has_code":false}