Detection of Abnormal Symptoms Using Acoustic-Spectrogram-Based Deep Learning

Acoustic data inherently contain a variety of information, including indicators of abnormal symptoms. In this study, we propose a method for detecting abnormal symptoms by converting acoustic data into spectrogram representations and applying a deep learning model. Spectrograms effectively capture the temporal and frequency characteristics of acoustic signals. In this work, we extract key features such as spectrograms, Mel-spectrograms, and MFCCs from raw acoustic data and use them as input for training a convolutional neural network. The proposed model is based on a custom ResNet architecture that incorporates Bottleneck Residual Blocks to improve training stability and computational efficiency. The experimental results show that the model trained with Mel-spectrogram data achieved the highest classification accuracy at 97.13%. The models trained with spectrogram and MFCC data achieved 95.22% and 93.78% accuracy, respectively. The superior performance of the Mel-spectrogram model is attributed to its ability to emphasize critical acoustic features through Mel-filter banks, which enhances learning performance. These findings demonstrate the effectiveness of spectrogram-based deep learning models in identifying latent patterns within acoustic data and detecting abnormal symptoms. Future research will focus on applying this approach to a wider range of acoustic domains and environments. The results of this study are expected to contribute to the development of disease surveillance systems by integrating acoustic data analysis with artificial intelligence techniques.