# Automated Personalized Loudness Control for Multi-Track Recordings
## 1 Introduction
These contributions highlight the increasing reliance on sophisticated algorithms to address complex challenges in audio engineering, aligning well with the ongoing developments in automated mixing technologies. The significance of our work lies in its personalized approach to automated music mixing, distinguishing it from existing methodologies that generally aim for a one-size-fits-all solution. Unlike the broad strategies employed in previous studies, our research focuses on emulating the unique preferences and styles of individual engineers. This distinction is crucial because artists often seek out specific engineers for their distinctive sound and approach to mixing. The ability of our algorithm to learn and adapt to the preferences of different engineers represents a paradigm shift in automated mixing technology. It not only automates the technical aspects of mixing but also captures the creative signature essence of the engineer, thus offering a more tailored and artistically sensitive solution. Our methodology incorporates the pyloudnorm Python system, which adheres to the ITU-R BS.1770-4 standard and EBU R128 recommendation for measuring loudness [PERSON] et al. (2018). By using the Loudness Units Full Scale (LUFS) meter, we can measure the volume in a manner that is consistent with both human perception and industry norms. Through the process of measuring and standardizing the loudness of each track, we provide a uniform starting point for our algorithm. This guarantees that the final mix attains the intended aural equilibrium and quality. In this paper, we further investigate the implementation of automation in music mixing by proposing an experimental configuration that evaluates an individualized multi-track leveling method across a total of 20 songs and 10 engineers. This approach, which is based on our prior research [PERSON] et al. (2018), aims to improve the automation of the mixing process by focusing on crucial components such as loudness control and reproduction of creative decision-making similar to that of skilled audio engineers. The current experiment aims to evaluate the capacity of the algorithm to adjust to different musical genres and multiple engineering preferences, thus tackling the difficulty of automating music mixing with a sophisticated approach that considers the nuances of creative intention and technological implementation. Through the analysis of the algorithm's performance on a wide range of songs and mixing styles, our goal is to confirm its effectiveness and adaptability, which is a crucial milestone in addressing the challenges of automating music mixing. In this article we seek to enhance our previous discoveries from [PERSON] et al. (2018) and make a substantial contribution to the area by showcasing an advanced technique for obtaining customized loudness control and innovative decision-making in automated music mixing. This paper is organized as follows. In Section 2, details about the used dataset, signal-processing operations and our applied methods for loudness control are presented. The results of our work are highlighted in Section 3. An analysis of the experiments is carried out as a discussion in Section 4. Finally, Section 5 concludes this work.