Kaja Wierucka

This study examines how dominance rank affects behavioral responses to contact calls in ring-tailed lemurs, finding sex-specific effects where high-ranking females influence group movement.

This study links automated detection of alpaca hums with GPS data to show that hums are associated with closer group contact and faster movement, suggesting a role in social cohesion.

VesperFlow automates decoding of multisensor biologging data, validating on lemur field data to process movement, vocal, and spatial data reliably.

This study shows that food calls in marmosets predict sharing decisions, with acoustic features influencing whether food is transferred or not.

This study shows marmoset phee calls encode external events like food context beyond emotional arousal and valence, even without an immediate stimulus.

This study shows that common marmosets adjust their vocal sequence structure, but not acoustic features, during pair formation, suggesting sequence-level vocal convergence driven by social bonds.

The paper evaluates machine learning methods for caller identity classification in mammalian vocalizations, finding Mel-frequency cepstral coefficients and random forests provide consistent results across datasets.

The paper examines if alpaca hums encode individual identity through acoustic features and finds spectro-temporal features provide more accurate classification than MFCC.

The paper analyzes the acoustic structure and informational content of common marmoset phee sequences, finding that elements encode caller identity and sex with varying accuracy across positions.