We develop and leverage machine learning algorithms for annotating large datasets of canary songs to study the maintenance and changes of their rich repertoires across different time scales and social contexts. We combine realtime behavior manipulation by targeted perturbations with detailed video tagging and motion analysis to characterize the rules governing song syntax adaptation and perception by its target audience