Heart sound classification is a critical task in automated cardiac diagnostics, yet it is often challenged by the limited availability of labeled data and the dominance of low-frequency components in heart sound signals. This study introduces a novel data augmentation technique, the first-difference method, to address these challenges in convolutional neural network (CNN)- based classification. By enhancing high-frequency components in the time domain, this method enables the model to better capture abnormalities, such as murmurs, present in higher frequency ranges. The effectiveness of this approach was evaluated using three spectral transformations—linear spectrogram, mel-spectrogram, and mel-frequency cepstrum coefficient (MFCC) —across multiple augmentation levels. The results demonstrate substantial improvements in classification metrics, including precision, recall, F1 score, and specificity, with MFCC-based predictors achieving the highest performance gains. The findings highlight the potential of the first-difference augmentation as a simple and effective strategy for improving heart sound classification, paving the way for more robust and generalizable diagnostic tools in real-world clinical applications.