Automated localization of calling birds with small passive acoustic arrays in complex soundscapes

Abstract

Accurately localizing vocalizing animals from passive acoustic recordings remains challenging in complex outdoor soundscapes. Although automated detection and classification of bird calls have advanced rapidly, reliable spatial localization has lagged, particularly for small arrays of autonomous recorders operating without manual intervention. Here we describe a fully automated pipeline for three-dimensional localization of bird vocalizations using distributed networks of 4 to 8 GPS-synchronized recorders deployed in heterogeneous forest-adjacent environments. Our framework integrates automated multi-recorder event matching, frequency-selective FFT-based cross-correlation for time-difference-of-arrival (TDOA) estimation, geometric cycle-consistency filtering to resolve ambiguous cross-correlation peaks, and nonlinear optimization of source location and effective sound speed. We applied this pipeline to recordings collected from October 2023 through May 2026 at three sites in southwest Arkansas. Of 6,037,004 non-redundant high-confidence bird detections across 258 species, 1,381,317 passed our criteria for potential localization, of which 1,055,021 (76.4%) spanning 174 species yielded positional estimates. The localizations exhibit strong concordance with independently known landscape features and species-specific habitat associations. These analyses indicate that small, practical arrays can recover ecologically meaningful spatial structure in complex soundscapes without manual curation. This preprint documents the current state of the system and its performance under realistic field conditions.