## 4 Experiments  
We collected and arranged data in the form of database, which consist of two sets of data: normal set and abnormal set, the whole data in database (normal and abnormal) have five categories, one normal category (N) and four abnormal categories, the four abnormal categories were: aortic stenosis (AS) mitral stenosis (MS) mitral regurgitation (MR) mitral valve prolapse (MVP), details of data is shown in Table 1, there complete detail (w.r.t to category) were discussed in background section. The total numbers of audio files were 1000 for normal and abnormal categories (200 audio files/per category), the files are in.wav format. Our data collection procedure outline is shown in Figure 4b, in which we first selected cardiac disorder category, collected data then applied data standardization after filtering [Appendix A].  
After gathering the data, the experiments we performed, can be categorized into nine types, as discussed in methodology section, we did first 3 experiments using SVM classifier and three types of features: MFCCs, DWT and MFCCs + DWT (fused features), second three experiments using DNN and last 3 experiments using centroid displacement based KNN, each of these features were trained and classified using each classifier individually. In case of centroid displacement based KNN we choose the value of k = 2.5 and that worked well for our dataset training. [24].  
For training and classification of MFCCs features using SVM the parameters and arguments we used while extracting MFCCs features were as follows: sampling frequency 8000 Hz, feature vector having length 19 and frame size in sample 240 with step size of 80.Similarly, for training and classification of DWT features using SVM the parameters and arguments we used while extracting DWT features were as follows: sampling frequency 8000 Hz, and length of feature vector 24. Using fivefold cross validation technique, we classified our data using SVM and DNN. From dataset, 20% of sample data in each class was used for testing and eightty percent of sample data in each class for training,  
\begin{table}
\begin{tabular}{c c c} \hline  & **Classes** & **Number of Samples Per Class** \\ \hline Normal & N & 200 \\ \hline \multirow{4}{*}{Abnormal} & AS & 200 \\  & MR & 200 \\ \cline{1-1}  & MS & 200 \\ \cline{1-1}  & MVP & 200 \\ \hline Total & & 1000 \\ \hline \end{tabular}
\end{table}
Table 1: Dataset detail.  
Figure 4: (a) Neural network composed of many interrelated neurons; (b) Data collection procedure outline.  
the performance of each machine learning algorithm with different features were recorded with respect to their given accuracies.  
Referring to Figure 5, the data training phase has been shown, where data and features are distributed clearly shows that in case of DWT, the data is spread more across its region in plan, as it represents both lower and higher frequencies on its axes hence better performance evaluation.  
Figure 6 shows confusion matrices for features type and the training model, as explained in the footer section of this figure, a; d; g belongs to MFCCs features, b, e, h belongs to DWT features and c, f, i belongs to combined features (MFCCs plus DWT).  
The heart sound signal preprocessing and classification is performed by three different methods and classifiers. In case of any speech and signal processing, study shows that features extracted with wavelets transform have greater performance and when those features are used in combination with MFCCs, the accuracy goes even higher [5; 6; 25].  
Figure 5: (a) MFCCs Features data pattern; (b) DWT Features data pattern; (c) DWT plus MFCCs Features data pattern.  
Figure 6: _Cont._