# Automated Personalized Loudness Control for Multi-Track Recordings
## 1 Introduction
Engineers could use diverse strategies to manage track loudness, which might be influenced by their distinct views, target audience and the specific music genre they are working on. In the domain of automatic music mixing, significant strides have been made towards developing algorithms that can emulate the nuanced decisions of human engineers. The inception of 'Automatic Mixing' can be traced back to 1975 with [PERSON]'s speech application focusing on microphone gain control [7]. Significant contributions to the field include the work of [PERSON] et al. [8], who introduced an automatic maximum gain normalization technique that forms the basis for many subsequent developments in audio mixing automation. Their methodology, detailed in the Journal of the Audio Engineering Society, provided a cornerstone for gain and fader control algorithms in live and recorded music scenarios. [PERSON] et al. [9] focused on the analysis of acoustic features for automated multi-track mixing, offering insights into how different elements of a mix can be automatically balanced to achieve a desired sonic quality. Their work underscores the importance of understanding the acoustic characteristics of individual tracks for effective automation. [PERSON] et al. [10] contributed to the field with their research on multi-track mixing using models of loudness and partial loudness. Their approach, aiming to improve the clarity and balance of mixes by addressing energetic masking, demonstrates the complexity of achieving a well-balanced mix through automated processes. [PERSON] et al. 's [11] research on automatic mixing using modified loudness models presents a method for level balancing that considers the psycho-acoustic phenomenon of loudness perception. This work is pivotal in understanding how listeners perceive the loudness of different sources within a mix and how this perception can be modeled and applied in automatic mixing systems. [PERSON] et al. 's [12] exploration of automatic mixing level balancing, enhanced through source interference identification, adds a layer of sophistication to automated mixing by addressing the issue of source interference. Their work proposes solutions for identifying and mitigating interference between sources, thereby enhancing the clarity and quality of the mix. [PERSON] et al. 's [PERSON] et al. (2018) research addresses the challenge of achieving desired audio outcomes by leveraging the flexibility and efficiency of genetic algorithms, which can evolve solutions based on user preferences and feedback. This method stands out by facilitating a more intuitive interaction between the user and the mixing process, enabling the user to influence the evolution of the mix in real time. Further emphasizing the role of technological advancements in audio processing, recent studies in uncertainty estimation provide a crucial foundation for enhancing automated mixing systems. [PERSON] et al. [PERSON] et al. (2018) discuss the integration of probabilistic source separation with feature extraction to robustly estimate audio features despite errors in separation, particularly useful in under-determined scenarios such as polyphonic music recordings. Extending to multimodal contexts, [PERSON] et al. [PERSON] et al. (2018) introduce an uncertainty-driven hybrid fusion model for audio-visual phoneme recognition, which adaptively manages unreliable visual inputs to enhance accuracy. Additionally, [PERSON] et al. [PERSON] et al. (2018) tackle the challenge of source feature extraction in multi-source recordings by employing deep learning to handle uncertainty, aiming to improve both the accuracy and robustness of separations. [PERSON] et al. [PERSON] et al. (2018) provide a novel approach to the challenge of compressive blind mixing matrix estimation of audio signals, focusing on directly estimating the mixing matrix from compressive measurements without the need for reconstructing the mixtures to enhance the speed and accuracy of audio signal processing.