## 4 Experimental Results
### Comparison of Internal Features
\begin{table}
\begin{tabular}{l l l} \hline \hline
**S. No.** & **Features Used for Comparison** & **Accuracy in Percentage** \\ \hline \hline Individual Data & & \\ \hline
1 & Musical notes & 54\% \\
2 & Pitch data & 31\% \\
3 & Census data & 15\% \\
4 & Provenance data & 19\% \\ \hline \hline Combination Data & & \\ \hline
5 & Musical chords and Pitch data & 63\% \\
6 & Musical chords and census data & 58\% \\
7 & Musical notes and provenance data & 60\% \\
8 & Pitch data and census data & 56\% \\
9 & Pitch data and provenance data & 58\% \\
10 & Census data and provenance data & 49\% \\
11 & Musical notes, pitch, and census data & 74\% \\
12 & Pitch data, census data, and provenance data & 68\% \\
13 & Musical notes, pitch, and provenance data & 81\% \\
14 & Musical notes, census, and provenance data & 76\% \\
15 & All the features & 86\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Analysis of the results from an internal comparison. \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**S.no**} & \multicolumn{1}{c}{**Copied Songs**} & \multicolumn{1}{c}{**Influenced Songs**} & \multicolumn{1}{c}{**Same Genre**} & \multicolumn{1}{c}{**No Match**} \\  & \multicolumn{1}{c}{**(Class-1)**} & \multicolumn{1}{c}{**(Class-2)**} & \multicolumn{1}{c}{**(Class-3)**} & \multicolumn{1}{c}{**(Class-4)**} \\ \hline  & Dazed and confused & Joyful Noise (Da T.R.U.T.H) & Jazz & See you Again ([PERSON] \\  & ([PERSON]) & v.s & & So what ([PERSON]) & [PERSON], [PERSON]) \\
1 & vs.s & Dark Horse ([PERSON]) & v.s & v.s \\  & Dazed and Confused & & My one and only Love & My rifle, my pony, and me ([PERSON]) \\  & (Led Zeppelin) & & ([PERSON]) & \\ \hline  & You need love ([PERSON] & If I Could Fly ([PERSON]) & Techno & All the stars \\  & [PERSON]) & v.s & & ([PERSON], SZA) \\
2 & v.s & Viva la Vida (Coldplay) & Energy Flash ([PERSON]) & v.s \\  & Whole lotta love & & v.s & Mother Sky (Can) \\  & (Led Zeppelin) & & Bang the box (Jackmaster) & \\ \hline  & The Who ([PERSON]) & Let’s Get it On ([PERSON]) & Country & Danger Zone ([PERSON] \\  & v.s & v.s & & Loggins) \\
3 & You air’t seen nothing & Thinking out Loud & Abilene ([PERSON] & v.s \\  & yet ([PERSON] turner & ([PERSON]) & vs & Eye of the Tiger \\  & overdrive) & & I Walk the Line & (Survivor) \\ \hline  & Under Pressure (Queen & I wanna be your Boyfriend & Rock & Miss Misery \\  & and [PERSON]) & (The Rubinos) & You Shook Me All night long & ([PERSON]) \\  & v.s & v.s & (AC/DC) & v.s \\
4 & Ice baby (Vanilla ice) & Girlfriend ([PERSON]) & & Save Me ([PERSON]) \\  & & & & Sweet Child O’Mine \\  & & & (Guns n Roses) & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Sample set from dataset developed by experts from internal evaluation. shows the \(F\)-test on the regression model. This proves that the model fits significantly better than a degenerate model that consists of only a constant term. In Table 10, we compare models based on various combinations of features and provide their similarity scores. The combination of two data features, music notes and pitch data, provides the highest similarity scores; this is followed in order by the notes-provenance, notes-census, pitch-provenance, and census-provenance combinations. Furthermore, in a combination of three data features, the notes-pitch-provenance data provides the highest accuracy, followed by the notes-pitch-census data, and then the pitch-census-provenance data. According to Table 11, musical notes are the most significant individual data feature, followed in order by the census, pitch, and provenance features. Individually, provenance data performed poorly, but in combination with musical chords and other data features, its inclusion in the model improves accuracy significantly. We can also observe the individual impacts of these features on the overall accuracy. From the internal comparison phase, we can conclude that the accuracy of TruMuzic improves with an increasing number of features in the input data.