# Automated Personalized Loudness Control for Multi-Track Recordings
## 3 Results
This method helped in reducing any variations in the listening environment that could potentially impact their evaluations of the audio quality. For the subjective listening test described, the engineers used the same digital audio workstation (DAW) software that they are most accustomed to for mixing to conduct the evaluations. This choice is significant as it ensures that the engineers are working within a familiar audio processing environment, reducing the learning curve and potential biases that might arise from unfamiliarity with new software tools. This ensured an impartial evaluation. The engineers were instructed to listen to each version 3 times in order to acquaint themselves with the subtleties of each mix. After participating in these listening sessions, participants were given the task of evaluating each version using the ITU standard grading scale, which ranges from 1 to 5. A rating of 1 indicates the lowest quality, while a rating of 5 indicates the highest quality. The average of these three scores was then calculated to generate a final score for each mix version for every engineer. The results for the training dataset are presented in Tables 1 and 2 for validation. Upon reviewing the listening test results for the 15 songs used to train the algorithms, we observe that the genetic algorithm achieved a score that was equal to or greater than the human-engineered mix in half of the cases, specifically for 5 out of the 10 engineers. The CNN also performed notably, matching or surpassing the MIX in 4 out of the 10 instances. In the scenarios where the GA and CNN did not outscore the MIX, their performance remained close to the reference, indicating a marginal difference. This tight competition between the algorithms and the human MIX scores underscores the sophistication and potential of these automated mixing tools to closely approximate, if not enhance, the nuanced craft of human audio engineering. In the validation phase of our study, we assessed the performance of the algorithms on a set of 5 complex songs, selected for their challenging nature in terms of mixing. The results for these songs are revealing in terms of the capability of both GA and CNN to handle complicated audio tasks. The RAW tracks, which had no processing, received scores that highlight the inherent difficulties present in the unaltered recordings, with mean scores ranging from 1.4 to 3.7 across the engineers. In contrast, the human-engineered MIX versions, which represent the benchmark of professional quality, scored significantly higher, with means spanning from 2.8 to 4.7, reflecting the engineers' ability to enhance and refine the tracks. Notably, the GA and CNN both showed strong performances in this challenging scenario. The scores from GA were consistently competitive, ranging from 2.2 to 4.3, closely trailing the MIX scores and, in some cases, matching them. The CNN mirrored this robust performance with scores from 2.0 to 4.3, demonstrating that the advanced modeling and learning capabilities of CNNs are effective even with the most complex mixes. These results suggest that both automated methods are adept at navigating the intricacies of sophisticated song structures and mixing requirements. Their performance in this validation set reinforces the potential of these algorithms to act as valuable tools in the mixing process, providing high-quality results even when confronted with demanding audio material. In the realm of audio engineering, professionals are often distinguished by varying levels of proficiency, each characterized by distinct skill sets, experiences and contributions to the music-production process. Our participant engineers might be categorized based on their competence level as follows:  
* S1 and S6: Professional Engineers (Pro);
* S8, S9 and S10: Semi-Professional Engineers (Semi-Pro);
* S5 and S7: Aspiring Engineers (Aspiring);
* S2, S3 and S4: Amateur Engineers (Amateur).