Automated localization of calling birds with small passive acoustic arrays in complex soundscapes
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 6 GPS-synchronized recorders deployed in heterogeneous forest 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. Applied to multi-year datasets from three field sites, the localizations exhibit strong concordance of localizations 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.