# Understanding Vocalization Might Help to Assess Stressful Conditions in Piglets

## TL;DR

This study shows that analyzing piglet vocalizations can help detect stress from pain, cold, and hunger during the farrowing phase.

## Contribution

The study introduces vocalization analysis as a noninvasive method to assess stress in piglets during farrowing.

## Key findings

- Piglet vocalizations can be used to classify stress conditions like pain, cold, and hunger with 81.12% accuracy.
- Vocalization analysis is a feasible noninvasive tool for assessing piglet welfare during the farrowing phase.
- Stressful conditions such as cold and hunger were reliably identified through vocal signal patterns.

## Abstract

This research aimed to analyze the possibility of assessing piglets’ welfare using the records of their vocalization. The trial was done in a pig commercial farm, and we recorded the vocal signals from piglets in several stressful exposure situations. Data mining techniques were applied to the processed signals in order to obtain a stress classification using the recorded data. We found that, using the piglets’ vocalization, it was possible to identify the most frequent stressful conditions at the farrowing phase, namely: pain, cold and hunger.

Assessing pigs’ welfare is one of the most challenging subjects in intensive pig farming. Animal vocalization analysis is a noninvasive procedure and may be used as a tool for assessing animal welfare status. The objective of this research was to identify stress conditions in piglets reared in farrowing pens through their vocalization. Vocal signals were collected from 40 animals under the following situations: normal (baseline), feeling cold, in pain, and feeling hunger. A unidirectional microphone positioned about 15 cm from the animals’ mouth was used for recording the acoustic signals. The microphone was connected to a digital recorder, where the signals were digitized at the 44,100 Hz frequency. The collected sounds were edited and analyzed. The J48 decision tree algorithm available at the Weka® data mining software was used for stress classification. It was possible to categorize diverse conditions from the piglets’ vocalization during the farrowing phase (pain, cold and hunger), with an accuracy rate of 81.12%. Results indicated that vocalization might be an effective welfare indicator, and it could be applied for assessing distress from pain, cold and hunger in farrowing piglets.