DCATVD: A Large-Scale Longitudinal Dataset for Analysis of Domestic Cat Vocal Development

In longitudinal studies of animal vocalizations, machine learning models enable a deeper analysis and understanding of developmental patterns in animal communication and vocalizations. However, longitudinal studies remain constrained by the limited availability of high-quality, large-scale datasets. To address this gap in feline bioacoustics, this paper introduces the Domestic Cat Age Transition Vocalization Dataset (DCATVD), a large, robust dataset containing vocalizations from 135 individual domestic cats across 12 different breeds with precise metadata (exact birth-date, breed, age group, and individual cat ID). Our thorough analysis of this dataset reveals novel discoveries on how essential vocal variables develop across the lifespan of cats, such as shifts in dominant vocalization type from purr to meow and the increase of specific precise vocalization units (Elemental Cat Meow Units). This paper opens an avenue to advance bioacoustic representation learning through an accessible dataset and a reproducible pipeline.