# Automated Personalized Loudness Control for Multi-Track Recordings
## 3 Results
In reinterpreting the data based on the level of proficiency, we observe distinct trends in how the different engineer categories perceive the quality of mixes produced by the \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Version** & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** & **S8** & **S9** & **S10** & **All** \\ \hline original (RAW) & 3.5 & 3.9 & 3.5 & 3.1 & 3.5 & 2.9 & 3.7 & 1.2 & 3.5 & 1.5 & 3.0 \\ artist’s mix (MIX) & 4.3 & 4.1 & 4.0 & 3.7 & 3.3 & 3.3 & 4.3 & 2.2 & 4.0 & 4.0 & 3.7 \\ genetic algorithm (GA) & 4.6 & 3.9 & 4.2 & 3.6 & 3.1 & 3.4 & 3.9 & 2.9 & 4.2 & 4.5 & 3.9 \\ neural network (CNN) & 4.5 & 4.2 & 4.5 & 3.7 & 3.1 & 3.7 & 4.1 & 3.3 & 4.5 & 3.7 & 3.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean listening scores over the training dataset of 15 songs. The last column represents the average over all 10 subjects for each of the 4 versions. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Version** & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** & **S8** & **S9** & **S10** & **All** \\ \hline original (RAW) & 3.4 & 3.7 & 3.6 & 3.2 & 2.2 & 1.6 & 3.5 & 1.4 & 3.6 & 1.6 & 2.8 \\ artist’s mix (MIX) & 4.7 & 4.0 & 4.3 & 3.8 & 2.9 & 3.2 & 4.6 & 2.8 & 4.3 & 4.0 & 3.9 \\ genetic algorithm (GA) & 4.3 & 3.2 & 3.8 & 2.8 & 3.2 & 2.6 & 3.8 & 2.2 & 3.8 & 2.2 & 3.2 \\ neural network (CNN) & 4.3 & 3.5 & 4.0 & 3.8 & 2.0 & 3.0 & 3.7 & 2.2 & 4.0 & 2.2 & 3.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean listening scores over the validation dataset of 5 songs. The last column represents the average over all 10 subjects for each of the 4 versions. algorithms compared to the raw and human-engineered mix. The Pro engineers rated their mix highest, which is expected as it aligns with their high standards and experience in creating polished mixes. However, it is noteworthy that both the GA and CNN also received high ratings from these engineers, often very close to their MIX scores. This suggests that the algorithms are producing results that approach professional quality, even in the eyes of seasoned engineers. Semi-Pro engineers provided a diverse range of scores. Interestingly, the algorithms, especially CNN, received scores that were competitive with the MIX, occasionally surpassing them. This indicates that the Semi-Pro engineers found the automatic mixed tracks to be of a high standard, suggesting that these tools could be particularly beneficial for those in this category looking to enhance their output. Aspiring engineers demonstrated a wide range of responses, possibly reflecting their developing ears and mixing preferences. The algorithms' scores were generally favorable, sometimes matching the MIX, which could indicate that these tools might serve as educational aids, helping aspiring engineers understand and achieve better mixing standards. For the Amateur engineers, the MIX and the algorithmic outputs were also rated better than the RAW version, with the algorithms often scoring on par with the human mixes. This reflects the potential for the GA and CNN to serve as effective tools for amateurs, helping them achieve a more professional sound that might otherwise be beyond their current skill level. In Figure 2, we chose to illustrate a comparison between the mean preferences of one Pro artist over the 20 songs, and the two automatic proposed methods (GA and NN). The loudness variance of the original artist mixes for all 20 songs is delimited in the rectangle blue boxes, showing significant variations between songs. The blue dots represent the mean computed over the 20 songs for the artist's coefficient for each track. The green pentagons are illustrated by the output of the genetic algorithm, and the red squares are the mean predicted coefficients from the neural network. As you can notice, both automatic methods are close to the user's mean preferences. The genetic algorithm will always output the same constant coefficients, which makes it a consistent method.