## 4 Experimental Results
### Comparison of Internal Features  
Our research must examine how much a particular data feature should affect the similarity comparison results using a linear regression model, as shown in Figure 4. This should help us know the impact of each feature set on the performance of our deep learning-based model, which is used to adjust the weight of each feature category. Next, we develop a dataset with the help of music experts, where we classify songs into four categories: 1. Copied songs; 2. Influenced songs; 3. Same genre; and 4. No match. Some examples of this are shown in Table 9. Additionally, Table 10 shows TruMuzic finding the similarity between these 16 songs and presents the model's individual and combined accuracy when considering combinations of the various features available in our data. For regression-based results, the coefficient of determination (R-squared) shows that a proportionate amount of variation in the response variable can be explained by the independent variable's variation in the linear regression model. The larger the R-squared is, the more variability is seen by the linear regression model. Generally, the higher the R-squared, the better the model fits the data. The model also displays the \(p\)-value for each coefficient. The \(p\)-values show which variables are significant for the model's result. The \(p\)-value is lowest for the musical notes data feature; the \(p\)-value indicates if this feature is statistically significant. If the \(p\)-value was found to be \(p<0.05\) (\(5\%\) significance level), then those variables are not considered significant and are removed from any further evaluation. Additionally, as shown in the graphs, the musical notes features have the smallest squared error (SE) and the highest t-coefficient, which further proves the significance of musical notes in our model. Overall, the regression-based results demonstrate that the inclusion of musical notes, along with pitch, census, and provenance data, enhances the predictive power of our model. These findings highlight the substantial influence of musical notes in determining the similarity between musical compositions and emphasize the importance of considering multiple data types for a comprehensive music similarity evaluation. Figure 4: Similarity features experimental results that illustrate the impact of music features on the similarity model. (**A**): Music notes; (**B**): pitch; (**C**): census data; (**D**): provenance data; (**E**): all features. The dominant variable can be determined by the slope of the fitted line. As the notes feature has the highest slope, it is the most significant feature, i.e., finding the ground truth is highly dependent on the notes. This finding is further strengthened by the \(p\)-value and squared error results for the notes feature. The fitted line shows how the model, as a group of variables, can explain the response variable. The slope of the fitted line is not close to zero, and while the confidence bound does not include a horizontal line, this shows that the model fits better than a degenerate model that consists of only a constant term. The test statistic values are shown in the model display (F-statistic vs. constant model), which