Logan S. James

This study identifies acoustic patterns in bird songs and human music that are shared across species and linked to production and cognitive mechanisms.

BirdCODE detects and classifies bird vocalizations from large audio datasets, enabling large-scale studies of bird communication with improved temporal resolution and species coverage.

The paper maps vocal repertoires to social behaviors in cooperative crows using machine learning and audio-visual data.

This study uses an AI model to analyze zebra finch vocal interactions and identifies key features of their communication rules.

This study shows humans share acoustic preferences with various animals, with stronger agreement for ancestral and lower frequency sounds, and links preferences to music experience.

The paper tests if multisensory male displays improve female memory in tungara frogs, finding that memory is enhanced after signal fluctuations.

This study shows that deviations from stereotyped syllable sequences and timing in zebra finches do not reduce vocal learning and can even enhance it in later-developing birds.