## 4 Experimental Results
### External Comparison
#### 4.2.2 Songs Famous for Allegedly Being Copied  
In this experimental setup, we used an extended version of the dataset presented in Table 9. Our team of music experts included 100 songs in each class to help evaluate the accuracy of our model. We computed the similarities of these pairs and compared the results against the established ground truths. The evaluation here indicates whether the model in question can classify the songs correctly as copied or not. We merely show the results of our analysis and do not claim that any of the songs were indeed copied or not. As shown in Table 12, we can see that the SiMPle method, edit distance, and [PERSON] et al. follow the ground truth somewhat closely, but [PERSON]'s model [7], MESMF, and TruMuzic are the models closest to the ground truth. Here, the ground truth is the correct classification of songs in the four different categories developed by our experts. As shown in Table 13, TruMuzic outperforms the existing models, since in this test we included provenance data, census data, pitch data, and time-based chord comparisons. As we can see in Table 13, TruMuzic consistently outperforms the other SOTA methods in all classes, except classes 1 and 3, where MESMF equals the performance of TruMuzic. The accuracy of TruMuzic is lowest when differentiating between classes 1 and 2 for copied and influential songs, respectively. However, when classifying between classes 1 and 4, TruMuzic reaches 100% accuracy. In the external experiments, it is slightly improper to compare other models with TruMuzic since it uses four data features to make its decisions, while the others use only data features based on the song's chords or its sheet music. Comparing Tables 12 and 13, we can see that the integration of pitch, census, and provenance data when calculating the final similarity scores enhances the accuracy of TruMuzic. As far as efficiency is concerned, TruMuzic is the slowest model, followed by edit-distance and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Set 1** & **Set 2** & **Set 3** & **Set 4** & **Set 5** \\ \hline Edit-distance [14] & 0.79 & 0.63 & 0.55 & 0.33 & 0.15 \\ [PERSON]’s Model [51] & 0.84 & 0.63 & 0.51 & 0.36 & 0.19 \\ SiMPle [12] & 0.90 & 0.66 & 0.52 & 0.38 & 0.19 \\ [PERSON]’s model [7] & 0.88 & 0.69 & 0.58 & 0.32 & 0.16 \\ MESMF [9] & 0.89 & 0.64 & 0.49 & 0.34 &.020 \\ DTW & 0.85 & 0.63 & 0.51 & 0.39 & 0.21 \\ \hline Ground-truth & 1.00 & 0.75 & 0.65 & 0.45 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Random restoration—an external comparison of DTW against other state-of-the-art methods. [PERSON] et al [51]. Moreover, the SiMPle method presented by [PERSON] et al. [12] outperforms all the other models in time-based efficiency. However, as explained earlier, we are focused on accuracy in this project. Moreover, to explain the impact of deep learning-based weight distribution, we have shown the results of TruMuzic with and without weight distribution in Table 13. The deep learning-based weight distribution improves the overall accuracy of our model by 24%. The TruMuzic database consists of a combination of test data, random songs, allegedly copied songs, and intentionally altered versions of original songs. We used databases such as kernscores, and cmme.org, along with several music transcription tools such as music21, scorecloud, etc., to search and generate data for our database. The data features we are using are challenging to find or produce for a large number of songs, even with our team of three music experts. As such, we limited the number of songs in our testing to 3800. For experimental purposes, we also used altered versions of some famous songs, for example, covers or remixed versions. The idea behind this was to generate a dataset that would include songs that represent real-world plagiarism problems well. We provide the details of our experts in Section 7. Another important aspect of comparative work in the field of music is cover song identification [46; 47].