In large-scale poultry farming, respiratory diseases affect the health of chickens, leading to a decline in the quality and yield of both meat and eggs. Effective monitoring of these diseases is crucial to reducing their impact and enhancing the quality and yield. Currently, most monitoring methods still rely on manually monitoring chicken vocalizations, which are time-consuming, labor-intensive, and require specialized personnel. Existing smart methods are often limited to laboratory environments where individual chickens are monitored separately. These approaches do not meet the industrial and commercial requirements of poultry farms, where a diverse set of complex auditory signals may be captured. These signals include not only chicken vocalizations but also complex noises from cages, human activities, mechanical ventilation systems, and other background noises.

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 wavelet scattering transform (WST) and Long Short-Term Memory (LSTM) network, and the use of preprocessed chicken vocalizations through a deep denoising scheme, adopting an audio image generation model (AIGM). We have used a public chicken language dataset consisting of a total of segments for each of the three categories (Healthy, Sick, None - no chicken sound), totaling 6,000 five-second audio clips from actual farming environments, which were labeled by veterinary experts. Promising robust performances are achieved by the proposed method outperforming the state-of-the-art methods for detecting poultry respiratory diseases, and enabling poultry personnel to accurately determine the health and well-being of the chickens.