Passive Acoustic Monitoring (PAM) is an increasingly common method for monitoring birds and other sound-producing organisms at scale, but methods that digest these data streams into ecological insight remain underdeveloped. PAM and classification algorithms powered by artificial intelligence (AI) provide a promising avenue to describe the phenology of vocal animals, but standardized methods with verified connections to biological phenomena are lacking. Here, we articulate hypotheses regarding the relationship between avian vocal activity and phenological events, and present a workflow for quantifying avian vocal phenology from PAM data. We applied our workflow to 18,568 hours of audio from 185 sites across Olympic National Park. Using expert-verified BirdNET data and hierarchical generalized additive models, we estimated daily vocal activity probabilities for 25 bird species, from which we derived standardized “phenometrics” describing the timing, duration, and shape of vocal patterns. Estimated phenometrics generally supported hypotheses: residents exhibited earlier, longer vocal periods than migrants, and vocal activity was delayed and shortened in mid-elevations relative to lower elevations. Late-season vocal activity, particularly in resident and irruptive species, underscored PAM’s potential to capture ecological dynamics beyond breeding. We highlight opportunities for methodological advancement and the need to integrate PAM with field observations to strengthen biological interpretation.