## 3 Methodology
### Feature Extraction and Selection  
Based on the literature survey, the proposed work skipped the segmentation phase, based on the arguments of its high complexity and computation. Additionally, the research conducted by [44; 45; 46; 47] presented valid arguments for skipping the segmentation step. The selected heart sound recording datasets from the Physionet CINC Challenge [11] were used, as discussed previously. Since the dataset was not preprocessed for noise, it was extremely important to process each recording for noise removal. Figures 4a and 5a display the original state of the audio signal provided in the dataset. And, Figures 4b and 5b display \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Test**} \\ \hline \%\(\rightarrow\) & **Abnormal** & **Normal** & **Abnormal** & **Normal** \\ \hline Dataset a & 71.39 & 28.61 & - & - \\ Dataset d & 49.09 & 50.91 & 50.00 & 50.00 \\ Dataset e & 8.55 & 91.45 & 13.60 & 86.40 \\ Dataset f & 29.82 & 70.18 & - & - \\ Dataset g & - & - & 18.10 & 81.90 \\ Dataset i & - & - & 62.30 & 37.70 \\ \hline Total & 39.71 & 60.29 & 36.00 & 64.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Dataset composition after eliminating datasets ’b’ and ’c’. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Test**} \\ \hline \%\(\rightarrow\) & **Abnormal** & **Normal** & **Unsure** & **Abnormal** & **Normal** \\ \hline Dataset a & 67.50 & 28.40 & 4.20 & - & - \\ Dataset b & 14.90 & 60.20 & 24.90 & 15.60 & 48.80 & 35.60 \\ Dataset c & 64.50 & 22.60 & 12.90 & 64.30 & 28.60 & 7.10 \\ Dataset d & 47.30 & 47.30 & 5.50 & 45.80 & 48.80 & 8.30 \\ Dataset e & 7.10 & 86.70 & 6.20 & 6.70 & 86.40 & 6.90 \\ Dataset f & 27.20 & 68.40 & 4.40 & - & - & - \\ Dataset g & - & - & - & 18.10 & 81.90 & 00.00 \\ Dataset i & - & - & - & 60.00 & 34.30 & 5.70 \\ \hline Total & 18.10 & 73.00 & 8.80 & 12.00 & 77.10 & 10.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Training and test dataset composition. the harmonic and percussive component separation of the original audio signal, which was further processed for noise removal. Based on the findings in Table 1, the sound signal was clipped at 600 Hz. This preprocessing step is crucial as it ensures that the subsequent audio analysis operates on cleaner, noise-reduced signals, thereby improving the reliability and accuracy of this machine learning model in detecting and classifying audio features. To enhance the quality and accuracy of the audio analysis, the preprocessing for noise reduction on the audio files was performed using thinkDSP, which is a library for systematically filtering out high-frequency noise components and other audio processing functions. This preprocessing is based on a Discrete Wavelet Transform (DWT) using the Daubechies 6 (DB6) wavelet and a threshold value of 0.1. Additionally, a low-pass filter with a 600 Hz cutoff frequency was applied for further signal refinement. The DWT is particularly effective for analyzing non-stationary signals like PCGs because it provides a time-frequency representation, enabling localized analysis to isolate noise from the desired signal features. The DB6 wavelet, known for its compact support and orthogonality properties, was chosen for its ability to efficiently capture both smooth and transient features of the PCG signal, providing a balance between time and frequency localization. A soft thresholding technique with a threshold value of 0.1 was employed to reduce noise components while preserving the signal's important features. The methodology involved decomposing the PCG signal using the DWT with DB6, applying the threshold to specific coefficients, reconstructing the signal using the inverse DWT, and finally applying a low-pass filter with a 600 Hz cutoff frequency to eliminate any residual high-frequency noise (Figure 3 (DWT preprocessing)).