## 3 Methodology
### Datasets
#### 3.2.4 Reducing Volume  
Reducing volume is a data augmentation technique that has found extensive application in machine learning, particularly in the domain of audio signal processing. This technique aims to expand the diversity of training datasets by manipulating the amplitude or loudness of audio signals. This technique involves decreasing the amplitude of the audio waveform, thereby reducing the volume. The pseudocode for the volume-reducing augmentation technique is presented in Algorithm 4. ``` audio_data: Numpy array representing the original audio time series data. 0.0.



robustness, we carefully controlled the volume augmentation levels. The volume increase and decrease were applied using moderate scaling factors (\(\times 2\) and \(\times 0.5\), respectively) to ensure that the original emotional characteristics of the speech were preserved. Extreme volume distortions that could lead to misclassification were deliberately avoided. Figure 4 shows examples of data augmentations applied to the Saudi Dialect dataset using the techniques described above. The sample used corresponds to an angry emotion (01). The leftmost example represents the original sample (a) from the dataset, while the remaining examples illustrate augmented data generated through noise addition (b), time shifting (c), increasing volume (d), and reducing volume (e). Clear differences can be observed among the augmented data. For instance, in the noise addition example, random perturbations are introduced into the waveform, making it appear more irregular. In the time-shifting example, the waveform is shifted slightly along the time axis, altering its alignment without affecting its overall structure. Similarly, increasing the volume amplifies the waveform's amplitude, making it more pronounced, while decreasing the volume reduces the amplitude, resulting in a less prominent waveform. These variations demonstrate how each augmentation technique uniquely transforms the original data to improve the model's generalization and robustness. In addition to evaluating the efficacy of the four individual data augmentation techniques, we also assessed the performance of various combinations of these techniques. Specifically, the combinations included six pairwise arrangements, four configurations involving three augmentations, and one configuration incorporating all four augmentations. Table 1 provides an overview of the number of samples generated for each data augmentation configuration. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Number of Data Augmentations**} & \multicolumn{2}{c}{**Number of Data After Augmentation**} \\  & **Saudi Dialect Dataset** & **BAVED Dataset** \\ \hline Without augmentation & 175 & 1935 \\ With one augmentation & 350 & 3870 \\ With two augmentations & 525 & 5805 \\ With three augmentations & 700 & 7740 \\ With four augmentations & 875 & 9675 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of data in each number of data augmentations. Figure 4: Examples of the audio files with data augmentation: (**a**) original audio for an angry emotion (01), (**b**) noise addition, (**c**) time shift, (**d**) increasing volume, and (**e**) reducing volume. ### Feature Extraction

Mel Frequency Cepstral Coefficient (MFCC) is the most common feature extraction technique used for processing the human voice to calculate the cepstral coefficients with the consideration of human hearing. The block diagram in Figure 5 shows the steps to compute the MFCC. ### CNN Architecture

Convolutional neural networks (CNNs) are a prevalent tool in the field of speech emotion recognition, due to their capacity to automatically extract and analyze intricate emotional patterns in speech. In a CNN-based SER system, the input speech signal is converted into a visual representation, such as a spectrogram or MFCC, which enables the network to recognize the spatial and temporal features associated with the emotional content of the speech.