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Automated Personalized Loudness Control for Multi-Track Recordings.
- Source :
-
Algorithms . Jun2024, Vol. 17 Issue 6, p228. 15p. - Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *LOUDNESS
*SOUND engineers
*GENETIC algorithms
*CAPACITY (Law)
Subjects
Details
- Language :
- English
- ISSN :
- 19994893
- Volume :
- 17
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Algorithms
- Publication Type :
- Academic Journal
- Accession number :
- 178155244
- Full Text :
- https://doi.org/10.3390/a17060228