Automated Personalized Loudness Control for Multi-Track Recordings

Abstract

This paper presents a novel approach to automated music mixing, focusing on the optimization of loudness control in multi-track recordings. By taking into consideration the complexity and artistic nature of traditional mixing processes, we introduce a personalized multi-track leveling method using two types of approaches: a customized genetic algorithm and a neural network-based method. Our method tackles common challenges encountered by audio professionals during prolonged mixing sessions, where consistency can decrease as a result of fatigue. Our algorithm serves as a ‘virtual assistant’ to consistently uphold the initial mixing objectives, hence assuring consistent quality throughout the process. In addition, our system automates the repetitive elements of the mixing process, resulting in a substantial reduction in production time. This enables engineers to dedicate their attention to more innovative and intricate jobs. Our experimental framework involves 20 diverse songs and 10 sound engineers possessing a wide range of expertise, offering a useful perspective on the adaptability and effectiveness of our method in real-world scenarios. The results demonstrate the capacity of the algorithms to mimic decision-making, achieving an optimal balance in the mix that resonates with the emotional and technical aspects of music production.