# Detection of Abnormal Symptoms Using Acoustic-Spectrogram-Based Deep Learning
## 1 Introduction  
Acoustic signals contain various types of information. For instance, human speech conveys linguistic content, while operational sounds or mechanical noise can provide insights into the condition of devices or systems. Therefore, proper analysis of acoustic signals may allow for inference about individual states or environmental conditions, enabling timely and appropriate responses [1]. For example, changes in resonance or non-semantic vocal patterns have been studied to detect neurological disorders such as stroke, Parkinson's disease, and Alzheimer's disease [2]. In addition, sounds such as coughing and breathing have been utilized in health monitoring applications [3]. Given these characteristics, animal vocalizations also serve as valuable sources of information. Recent studies have explored the detection of vocal patterns associatedwith specific conditions such as illness or stress in livestock, with the goal of improving productivity and enabling early disease monitoring [4,5]. In this study, we investigate the detection of abnormal symptoms in poultry vocalizations, focusing on chickens, the most commonly formed poultry in Korea. Chickens suffering from respiratory diseases often exhibit abnormal vocal signs such as coughing, sneezing, snoring, and wheezing [6]. These symptoms can vary depending on environmental factors within livestock facilities. Traditionally, farmers have identified abnormal vocalizations manually in the field, a process that is labor-intensive, time-consuming, and limited in accuracy when identifying the cause or type of a symptom. Earlier research in acoustic event detection primarily employed traditional machine learning algorithms. These methods typically relied on handcrafted feature extraction from acoustic signals, using parameters such as formant frequency, pitch, power spectral density (PSD), and root mean square (RMS), followed by classification using models such as support vector machines (SVMs), hidden Markov models (HMM), or dynamic time warping (DTW) [5, 6, 7, 8, 9]. While effective in some cases, these conventional approaches are constrained by the complexity and labor involved in manually extracting a large number of features. Moreover, they struggle with non-linear and unstructured data, which limits their scalability and generalization. To overcome these challenges, recent studies have incorporated artificial intelligence techniques to automate feature extraction from acoustic data. For example, convolutional neural networks (CNNs) have been used to classify respiratory abnormalities [10] and animal sounds [11], leveraging various acoustic feature vectors as inputs. In this study, we propose a method for detecting abnormal symptoms in acoustic signals using CNNs trained on spectrogram-based feature representations. Spectrograms offer a compact and informative visualization of the time-frequency structure of acoustic data and are computed using the short-time Fourier transform (STFT). These representations can be used directly as input for training deep learning models or visualized as images. Our proposed framework involves collecting raw acoustic data, extracting relevant features, and detecting abnormal symptoms using deep learning. Feature vectors such as spectrograms, Mel-spectrograms, and MFCCs are generated from the STFT and Mel-filter banks and used to train a CNN-based model. The deep learning model is built on a custom ResNet architecture with shortcut connections to improve training efficiency and stability in deeper networks. The representative ResNet architecture, ResNet50, offers high classification accuracy; however, its computational requirements limit its applicability in real farm environments, where lightweight and low-power devices are preferred. The proposed model achieves a performance comparable to ResNet50 while reducing model size from 270 MB to approximately 1.19 MB, making it suitable for deployment on edge or embedded systems.