# Detection of Abnormal Symptoms Using Acoustic-Spectrogram-Based Deep Learning
## 5 Conclusions  
This study proposed a deep-learning-based method for detecting abnormal symptoms using spectrogram representations derived from livestock vocalizations. By converting acoustic signals into time-frequency representations such as spectrograms, Mel-spectrograms, and MFCCs and applying a convolutional neural network architecture, the model was able to learn relevant acoustic patterns associated with abnormal vocal symptoms. Among the feature types evaluated, the model trained on Mel-spectrograms achieved the highest classification accuracy at 97.13%. This result demonstrates the importance of input representation in acoustic classification tasks, as Mel-spectrograms emphasize perceptually relevant frequency components through the use of Mel-filter banks. In contrast, models using spectrogram and MFCC inputs showed slightly lower performance, suggesting that some information may be lost or underutilized depending on the feature extraction process. The proposed model was also found to be significantly more lightweight than standard deep networks such as ResNet50, while maintaining comparable accuracy. This balance between performance and model size supports the feasibility of deploying the model in \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline SVM & 88.57 & 88.79 & 86.27 & 87.35 \\ Plain-CNN & 93.06 & 94.65 & 90.46 & 92.12 \\ ResNet50 & 97.85 & 98.14 & 96.57 & 97.29 \\ Proposed Model & 97.37 & 97.53 & 96.34 & 96.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison between proposed model and baseline models (Mel-spectrogram). \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy** \\ \hline Spectrogram & 95.22\% \\ Mel-Spectrogram & 97.13\% \\ MFCC & 93.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model performance by data type. resource-constrained environments, including on-device or edge-based systems used in real-time monitoring applications. In addition, the dataset used in this study was carefully constructed through a combination of field recordings from actual poultry farms and expert-validated annotations. The inclusion of unrelated animal sounds and environmental noise allowed for robust evaluation under conditions that simulate real farm environments. These design choices contributed to improving the model's generalization performance and practical applicability. Future research will aim to expand this approach to other livestock species by collecting and analyzing a wider range of vocalization data. Efforts will also focus on implementing real-time processing and lightweight deployment on edge devices to support continuous monitoring in farm environments. In particular, applying compact models to IoT-based farm systems is expected to enable non-invasive and continuous health monitoring across various livestock management settings. We hope that this study contributes to ongoing research in acoustic-based health monitoring and inspires further development of intelligent livestock management systems. Conceptualization, S.-Y.K. and H.-W.K. ; data curation, S.-Y.K. and H.-M.L. ; formal analysis, S.-Y.K. and C.-Y.L. ; funding acquisition, H.-W.K. ; methodology, S.-Y.K. and H.-W.K. ; investigation, S.-Y.K., C.-Y.L. and H.-M.L. ; resources, H.-W.K. ; software, S.-Y.K. ; project administration, H.-W.K. ; validation, S.-Y.K., H.-M.L. and H.-W.K. writing--original draft preparation, S.-Y.K. writing--review and editing, S.-Y.K., H.-M.L., C.-Y.L. and H.-W.K. All authors have read and agreed to the published version of the manuscript. This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2022-0-01000, Development of 5G Edge Computing SW for Flexible Healthcare to Support Mobile Customized Medical Services). Not applicable. The data presented in this study are available on reasonable request from the corresponding author.