# Detection of Abnormal Symptoms Using Acoustic-Spectrogram-Based Deep Learning
## 4 Experiments and Results  
### Experimental Setup  
In this study, livestock vocalization data were used for experimentation. The dataset consisted of audio recordings collected directly from operational livestock farming sites, along with publicly available open datasets that are free from copyright restrictions. Specifically, poultry vocalizations, including those from broilers and laying hens, were gathered from two commercial farms: a laying hen farm located in Wonju, Gangwon-do, and a broiler farm situated in Asan, Chungcheongnam-do, as shown in Figure 10. To supplement the dataset with vocalizations associated with clinically abnormal symptoms, additional recordings were conducted at the experimental poultry farm of the College of Veterinary Medicine at Seoul National University, Pyeongchang Campus.  
The recording environments may have included varying levels of background noise due to typical farm activity and the presence of multiple animals. To ensure data quality, vocalizations were carefully selected based on clarity, with an emphasis on minimizing background interference and avoiding occlusion from overlapping sounds produced by other birds.  
The classification of abnormal data was carried out in consultation with a faculty expert in poultry clinical veterinary medicine from the College of Veterinary Medicine at Seoul National University. According to the expert, poultry infected with respiratory diseases commonly exhibit distinguishable vocal symptoms such as sneezing or wheezing, which are often accompanied by phlegmy or labored breathing sounds. Based on this professional assessment, vocal samples suspected to be abnormal were initially identified and then subjected to a review process to ensure clinical relevance. As a result, only those vocalizations that exhibited clear indications of potential respiratory infection or disease were verified and incorporated into the abnormal symptom dataset.  
In addition to chicken vocalizations, the dataset also included non-poultry acoustic samples corresponding to Class 3 and Class 4, such as dog barking and cat meowing sounds. Although these sounds are not directly associated with poultry vocal behavior, they were incorporated as supplementary input for training and evaluating the deep learning model.  
Figure 10: Experimental Environment for Poultry Data Collection.  
The inclusion of unrelated animal sounds enables the assessment of the model's ability to accurately identify abnormal poultry vocalizations while effectively distinguishing them from non-target acoustic events. This serves as an important step in validating the model's robustness and applicability in real-world farm environments, where various background sounds frequently coexist. Moreover, training with a diverse range of acoustic inputs helps mitigate the risk of overfitting to specific datasets. It also contributes to enhancing the model's generalization performance across different types of acoustic scenarios [21].  
The data for Class 3 and Class 4 were obtained from two publicly available datasets: ESC-50 [22] and ARCA23K [23]. ESC-50 is a curated dataset consisting of 50 categories of environmental audio recordings, including vocalizations from domestic animals such as dogs, hens, pigs, and cows. ARCA23K (Automatically Retrieved and Curated Audio) is a large-scale dataset containing animal and nature sounds collected from various web sources, intended for use in machine learning research. In addition, Class 0 (normal poultry vocalization) data were also supplemented with samples from the ESC-50 dataset. This strategy helps prevent bias toward specific recording locations or environmental noise conditions and reduces the risk of overfitting to a single dataset.  
The full dataset was categorized into five distinct classes, each representing a specific type of acoustic data. Table 1 presents the number of samples used for training and testing in each class, while Table 2 summarizes the characteristics associated with each class label