## 6 Images-to-Spectrogram Image Transform
Viridis provides perceptual accuracy, as it is designed with evenly spaced color differences that allow users to distinguish variations in data more easily. It also maintains readability in monochrome formats, ensuring that important information is preserved even when printed or viewed in low-resolution settings. The smooth, linear color transitions of Viridis avoid abrupt shifts, making it easier to interpret differences in intensity or frequency. Additionally, Viridis ensures that small changes in data are accurately represented, reducing the risk of misinterpretation, which is particularly important for scientific visualizations. As shown in Figure 5, there is sudden color darkening or sudden appearance and disappearance of color lines in all classes except the class with no error. This shows that the Viridis option is ideal for spectrogram transformation. The convolution layer of the CNN method intends to extract characteristic features processing input samples with convolution filters [24]. The mathematical computing of two functions is described as convolution. By conducting element-wise multiplication on each element, the convolution computing in the CNN technique implements a filter or kernel function utilized for the raw data's transformation into processed data [25; 26]. Each window's output in a shift operation is determined by the total multiplication of the element information [27; 28]. The batch normalization (BN) technique is used to design a more regular convolutional neural network. Moreover, during training, the CNN extinction gradient becomes more resistant [29; 30]. Figure 5: Conversion samples from raw images to spectrogram images for each class. In the Rectified Linear Unit layer (ReLU), the full \"f(k) = max(0, k)\" formula is used for all inputs as the layer activation function [31; 32]. ReLU's derivative is more appropriate and performs faster for algorithms like backpropagation as it is simpler than the sigmoid function. The softmax function is typically used for the output in deep learning models [33]. The function converts the class scores from the fully connected layer to probabilistic values ranging from 0 and 1. The softmax function is denoted by \(S\big{(}a_{j}\big{)}\) in Equations (1) and (2). It obtains an N-dimensional input vector, then produces a subsequent input vector with N-dimensional, having values between 0 and 1 [34; 35]. Additionally, even though the softmax function is frequently chosen for the output layer in deep learning models, an SVM classifier can be preferred [36]. The exponential feature of the softmax function makes the differences across classes more certain. \[S\big{(}a_{j}\big{)}: \begin{bmatrix}a_{1}\\ \dots\\ a_{n}\end{bmatrix} \rightarrow \begin{bmatrix}s_{1}\\ \dots\\ s_{n}\end{bmatrix}  \tag{1}\]  
\[S\big{(}a_{j}\big{)}=\frac{e^{j}}{\sum_{k=1}^{n}e^{a_{k}}} \tag{2}\]  
Figure 6 represents the attention module utilized in this research. The gating signal vector, symbolized by \(g_{i}\), has a wider scale at the feature map of output for ith layer \"\((x_{i})\)\", determining the focus region for each pixel [37]. Equations (3) and (4) give a computed output by applying element-wise multiplication. \[output=\alpha_{i}\times x_{i} \tag{3}\]  
\[\alpha_{i}=\sigma\Big{(}\varphi^{T}\big{(}w_{x}{}^{T}x_{i}+w_{g}{}^{T}g_{i}+ b_{g}\big{)}+b_{\varphi}\Big{)} \tag{4}\]  
The linear transformations \(w\) and \(\varphi\) using the 1 \(\times\) 1 \(\times\) 1 dimensional convolution operator are the bias terms \(b_{\varphi}\) and \(b_{g}\), respectively. The weights of the attention modules are adjusted randomly at first when the entire deep architecture is trained from end to end [38]. In conventional neural networks, each layer provides information to the next layer. Each layer enhances the subsequent layer directly for the network having residual blocks,  
Figure 6: The illustration for the proposed attention layer. then advances to layers, which are two to three hops away [39]. In multi-layer networks, the gradient vanishing problem is decreased by residual blocks.