Identification and characterization of clusters of potentially new vocalizations in broiler chickens using advanced acoustic analysis

Abstract

This study investigates and characterizes vocalizations in broiler chickens from 1 to 35 days of age using advanced acoustic analysis and machine learning techniques. Understanding broiler behavior, particularly vocalizations, is crucial for improving animal welfare in both on-farm and laboratory conditions. While four known vocalizations (distress calls, short peeps, warbles, and pleasure notes) are well documented using sound analysis, there remains a gap in understanding the full vocal repertoire of broiler chickens, which may hold key insights into their emotional and physiological states. Using a deep learning-based vocalization recognizer and recursive clustering algorithms, we identified 42 distinct sound clusters - in addition to the 4 known vocalizations - from recordings of healthy broiler chickens, eventually narrowing them down to 10 key clusters that potentially represent novel vocalizations. These vocalizations were analyzed for their frequency, duration, and acoustical power, and their temporal distribution was examined. The findings suggest that broilers expand their vocal repertoire as they age, presenting a more diverse repertoire in the later stages of life. Despite the limited sample size and absence of statistical replicates, this study offers valuable insights into the complexity of broiler vocalizations. This research contributes to the growing body of knowledge on broiler auditory communication and opens new possibilities for automated vocalization monitoring in chicken farming.