Detection of Respiratory Diseases Based on Poultry Vocalizations Using Deep Learning

In this study, we design a deep learning-based intelligent recognition method capable of accurately distinguishing abnormal chicken vocalizations among complex sound signals. Our proposed framework is based on the wavelet scattering transform (WST) and a Long Short-Term Memory (LSTM) network, and uses preprocessed chicken vocalizations processed through a denoising scheme, adopting an audio image generation model (AIGM) based on rectified STFT (Short-Term Fourier Transform). We have used a public chicken language dataset that consists of a total of segments for each of the two categories (Healthy or Sick), totaling 4000 five-second audio clips from actual farming environments, which are labeled by veterinary experts. The proposed method achieves promising performance, outperforming state-of-the-art methods for detecting poultry respiratory diseases and enabling poultry personnel to accurately assess the health and well-being of the chickens.