# Detection of Abnormal Symptoms Using Acoustic-Spectrogram-Based Deep Learning
## 3 Proposed Method
### Design of the Acoustic Abnormal Symptom Detection Model  
The proposed model visualizes and utilizes spectrograms, Mel-spectrograms, and MFCCs extracted from acoustic data as input for CNN-based training. The model architecture is based on a customized ResNet structure incorporating residual connections and Bottleneck blocks, as illustrated in Figures 8 and 9. The Bottleneck structure is composed of sequential \(1\times 1\), \(3\times 3\), and \(1\times 1\) convolutional layers. It reduces the number of parameters and removes irrelevant noise while preserving essential features [19]. This structure enables efficient parameter usage even in deeper layers of the network and contributes to a lighter model design with a lower computational cost. Residual connections are implemented using convolutional blocks, which include \(1\times 1\) convolution layers in the shortcut paths. These connections standardize feature map dimensions and maintain compatibility between layers [14]. Pooling operations are applied selectively within the first and third Residual Blocks to prevent excessive downsampling and ensure that spatial resolution is preserved. Figure 9 illustrates the architecture of the custom ResNet-based model developed in this study for detecting abnormal symptoms in acoustic signals. The model accepts spectrogram, Mel-spectrogram, and MFCC feature representations as input and extracts their salient patterns through a series of convolutional operations, activation functions, and max-pooling layers. The process begins with convolutional layers that generate feature maps by applying learnable filters to the input data. These feature maps are then standardized using batch normalization, which ensures that values are distributed within a normalized range (typically between 0 and 1). Batch normalization helps equalize the contribution of each feature, stabilizes the learning process, and accelerates convergence by mitigating shifts in the input distribution of each layer during training. Following normalization, the data pass through an activation function (ReLU), which introduces non-linearity to the model. This non-linear transformation is essential for enabling the network to learn complex and abstract patterns. The resulting activated feature maps are subsequently downsampled through max-pooling operations. Pooling reduces the spatial dimensions of the data, enhances computational efficiency, and retains the most dominant features for further processing. The pooled feature maps are then propagated through multiple Residual Blocks. These blocks progressively reduce dimensionality while simultaneously refining the extracted features. The residual connections within each block enable the network to bypass specific layers by directly adding the input to the output of subsequent layers. This architectural design addresses the vanishing and exploding gradient problems commonly encountered in deep networks, ensuring more stable and efficient training as depth increases. Furthermore, the inclusion of both batch normalization and dropout techniques within the model contributes to improved generalization performance. While batch normalization maintains consistent input distributions across layers,  
Figure 8: Standard and Bottleneck structures of the Residual Blocks. dropout randomly deactivates a portion of neurons during training, preventing overfitting by reducing co-adaptation of features. These techniques work in synergy to support a deeper and more robust network architecture capable of accurately detecting abnormal acoustic symptoms from diverse input formats [20]. The Residual Blocks in the proposed model are implemented using a Bottleneck structure, which first reduces the dimensionality of the input feature maps and then restores it. This approach effectively filters out irrelevant noise while preserving critical features, thereby enhancing computational efficiency without compromising model performance.