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Automated identification of chicken distress vocalisations using deep learning models
Annual global production of chickens exceeds 25 billion, and they are often housed in groups numbering thousands. Thus, assessing their welfare is very challenging. The amount of chicken distress calls could be used as an “iceberg indicator” of welfare because it’s linked to growth and mortality levels. We developed an algorithm to automatically identify chicken distress calls using recordings from an intensive farm. We correctly identified 97% of distress calls, among other farm sounds. This research will hopefully allow the development of precision livestock farming technology that will seek to monitor and reduce the stress experienced by chickens during production.