# Automated Personalized Loudness Control for Multi-Track Recordings
## 5 Conclusions  
In this paper, we present a novel approach that centers around a highly personalized multi-track leveling algorithm for automatic mixing. This implementation is designed to reflect the individual preferences of any user by learning their specific style and applying these preferences to the mixing process. We provide two algorithms for resolving the automatic loudness control: a customized genetic algorithm and a convolutional neural network, where both are adept at adjusting to the unique requirements of the user and executing precision-based loudness control across a multitude of tracks. We created a dataset of 20 songs, ranging from diverse genres and complexity. Both the genetic algorithm and neural network were trained on 15 songs and validated on 5 other songs, where the validation set was chosen as the most different musical pieces compared to the others from the dataset. This creative approach presents a novel multi-track leveling technique specifically designed to greatly simplify the mixing process for music producers and sound engineers. Upon receiving a new set of multi-tracks for a song, our approach not only efficiently organizes and arranges the tracks based on a specific order, but also does preliminary processing on each individual track, a process that is normally carried out manually. This preprocessing aligns each track with the precise mixing preferences of the engineer, guaranteeing that they are optimally prepared for import into the desired DAW. This tailored design enables producers to concentrate more effectively on enhancing and finalizing the remaining activities in the production process. This virtual assistant technology promotes creative freedom and makes high-quality mixing available to a greater variety of music enthusiasts and professionals by responding to the particular styles and requirements of each producer, thereby democratizing access.  
Conceptualization, B.M. and M.N.; methodology, B.M. and M.N.; software, M.N.; validation, B.M.; data curation, B.M.; writing--original draft preparation, B.M. and M.N.; writing--review and editing, C.P.; funding acquisition, C.P.; formal analysis, C.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS-UEFISCDI, project number PN-III-P4-PCE-2021-0780, within PNCDI III.  
Samples are available online on onedrive ([[https://shorturl.lat/kwFTX](https://shorturl.lat/kwFTX)]([https://shorturl.lat/kwFTX](https://shorturl.lat/kwFTX)) (accessed on 15 May 2024)).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Inteligence \\ CNN & Convolutional Neural Network \\ DAW & Digital Audio Workstation \\ DRC & Dynamic Range Compression \\ EBU & European Broadcasting Union \\ EQ & Equalization \\ GA & Genetic Algorithm \\ GPU & Graphics Processing Unit \\ ITU & The International Telecommunication Union \\ LUFS & Loudness Units relative to Full Scale \\ MAE & Mean Absolute Error \\ NN & Neural Network \\ RMS & Root Mean Square \\ \end{tabular}