What question did this study set out to answer?

This research examines how vocalizations in broiler chickens change with age and time of day, and whether they are affected by disease.

March 23, 2026Open Access

AI-based monitoring of broiler vocal repertoire dynamics reveals robust developmental and diurnal patterns but limited disease sensitivity

Key Points

This research examines how vocalizations in broiler chickens change with age and time of day, and whether they are affected by disease.
Utilized an AI-based vocalization detector to monitor vocalizations over a developmental period from 14 to 41 days of age.
Conducted experiments using two disease challenges: Eimeria spp. and IBV combined with APEC.
Recorded audio continuously from groups housed in pens and analyzed using a convolutional neural network.
Employed mixed-effects models for statistical analysis of vocalization data.
No significant changes in vocalization types were found due to disease challenges.
Age-related changes showed a decrease in distress calls, short peeps, and pleasure notes as broilers matured.
Vocalizations decreased at night compared to daytime periods from 14 to 28 days of age.

Abstract

Broiler vocalizations may contain relevant information about the welfare and health status; however, there is limited information on how disease affects vocal patterns. This study aimed 1) to investigate whether specific vocalization changes emerge in response to two important poultry diseases: intestinal ( Eimeria spp.) and respiratory (infectious bronchitis virus, IBV; combined with avian pathogenic Escherichia coli , APEC), and 2) to characterize changes in broiler chicken vocalizations over age (14–41 days of age), and across time of the day (morning: 6:00 AM–12:00 PM; afternoon: 12:00 PM–6:00 PM; evening: 6:00 PM–12:00 AM; night: 12:00 AM–6:00 AM). Vocalizations were monitored using an automated AI-based broiler vocalization detector previously developed by our group. A total of 176 Ross 308 broilers were included across four experimental rounds, each consisting of three treatments (control and the two disease challenges) with four replicates each of 11 birds per group. Groups were housed in 3 m² pens and continuously recorded from day 14 to day 41 using centrally mounted microphones. Disease challenges were administered on day 14 ( Eimeria spp.) and day 21 (IBV followed by APEC on day 24). Audio recordings were analyzed using a convolutional neural network trained to detect five vocalization categories: distress calls (DC), short peeps (SP), pleasure notes (PN), warbles (W), and other vocalizations (OV). Vocalizations were aggregated hourly to be evaluated across treatments, age and time of the day. Statistical analyses were performed using mixed-effects models in SAS (PROC MIXED). No significant effects of treatment were detected for all vocalization types ( p > 0.05). However, significant age-related changes in vocal expression were observed ( p < 0.05), with DC, SP, and PN decreasing progressively over age, while OV increased. Also, DC, SP, and OV were less frequent during the night from 14 to 28 days of age when compared to the other times of the day ( p < 0.05), indicating that vocal expression was primarily driven by age- and diurnal period-related changes rather than experimental disease challenge. These results highlight both the potential of vocalization patterns baselines to better understand broilers vocal behavior and current limitations of acoustic monitoring for detecting health disturbances in broiler chickens.

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