Exposing tail biters by automatic scream detection

Abstract

Tail biting is a major problem in pig husbandry and early intervention is essential to maintain animal health and welfare. To facilitate early identification of tail biters a detector software was created that can detect scream-linked biting events through analyzing video and audio recordings.Undocked weaner pigs housing in different pens and batches were recorded for a duration of 39 days. Scream-linked tail biting events were firstly identified manually and then automatically through the developed detector by analyzing the audio track of the video recordings. The detector's performance was evaluated by testing different sensitivity parameters (e.g., loudness threshold, call duration, frequency range).In four pens (748 h), a total of 2,898 screams were manually detected, with 52.8 % caused by tail biting. In the second step, 89.9 % of the detected screams were classified correctly as screams by the detector (precision 0.81–1.00). From the detected screams, 64.0 % were caused by tail biting. The detector automatically analyzes the latest videos in user-defined periods and sends warning messages if a threshold of screams per hour is exceeded. Then, biters can be identified manually using the detector extracted incidence video clips.In conclusion, automatic analysis of pig screams has high potential as an early intervention tool by detecting tail biting events followed by evaluation of time synchronized video clips for biter identification.

Keywords

• Tail biting
• Early detection
• Intervention
• Precision livestock farming
• Audio analysis