This study combines 1D CNN with advanced signal processing to enhance heart sound classification, presenting three key contributions. Initially, we used a pitch-shifting technique to expand the dataset by altering high-frequency components precisely, ensuring the preservation of vital information. Next, a signal normalization technique is deployed, equalizing signal lengths for uniform analysis across all samples. Utilizing 1D CNN and Mel-frequency cepstral coefficients (MFCCs) for feature extraction, our approach achieves notable classification accuracy, with results showing up to 99.57% accuracy, 99.80% specificity, 99.22% sensitivity, and a 99.22% F1 score. These developments not only advance the precision of heart sound classifications but also expand the potential for wider clinical applications, establishing a new benchmark in tele auscultation. This work is supported by Qatar University QUHI-CENG-23/24-216. The findings achieved herein are solely the responsibility of the authors. Scopus