A Machine Learning-Assisted Automation System for Optimizing Session Preparation Time in Digital Audio Workstations
Modern audio production workflows often require significant manual effort during the initial session preparation phase, including track labeling, format standardization, and gain staging. This paper presents a rule-based and Machine Learning-assisted automation system designed to minimize the time r...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2078-2489/16/6/494 |
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| author | Bogdan Moroșanu Marian Negru Georgian Nicolae Horia Sebastian Ioniță Constantin Paleologu |
| author_facet | Bogdan Moroșanu Marian Negru Georgian Nicolae Horia Sebastian Ioniță Constantin Paleologu |
| author_sort | Bogdan Moroșanu |
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| description | Modern audio production workflows often require significant manual effort during the initial session preparation phase, including track labeling, format standardization, and gain staging. This paper presents a rule-based and Machine Learning-assisted automation system designed to minimize the time required for these tasks in Digital Audio Workstations (DAWs). The system automatically detects and labels audio tracks, identifies and eliminates redundant fake stereo channels, merges double-tracked instruments into stereo pairs, standardizes sample rate and bit rate across all tracks, and applies initial gain staging using target loudness values derived from a Genetic Algorithm (GA)-based system, which optimizes gain levels for individual track types based on engineer preferences and instrument characteristics. By replacing manual setup processes with automated decision-making methods informed by Machine Learning (ML) and rule-based heuristics, the system reduces session preparation time by up to 70% in typical multitrack audio projects. The proposed approach highlights how practical automation, combined with lightweight Neural Network (NN) models, can optimize workflow efficiency in real-world music production environments. |
| format | Article |
| id | doaj-art-5dbcdf0d156c419fbac7655ef152917c |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj-art-5dbcdf0d156c419fbac7655ef152917c2025-08-20T03:27:33ZengMDPI AGInformation2078-24892025-06-0116649410.3390/info16060494A Machine Learning-Assisted Automation System for Optimizing Session Preparation Time in Digital Audio WorkstationsBogdan Moroșanu0Marian Negru1Georgian Nicolae2Horia Sebastian Ioniță3Constantin Paleologu4Faculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, RomaniaFaculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, RomaniaFaculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, RomaniaFaculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, RomaniaFaculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, RomaniaModern audio production workflows often require significant manual effort during the initial session preparation phase, including track labeling, format standardization, and gain staging. This paper presents a rule-based and Machine Learning-assisted automation system designed to minimize the time required for these tasks in Digital Audio Workstations (DAWs). The system automatically detects and labels audio tracks, identifies and eliminates redundant fake stereo channels, merges double-tracked instruments into stereo pairs, standardizes sample rate and bit rate across all tracks, and applies initial gain staging using target loudness values derived from a Genetic Algorithm (GA)-based system, which optimizes gain levels for individual track types based on engineer preferences and instrument characteristics. By replacing manual setup processes with automated decision-making methods informed by Machine Learning (ML) and rule-based heuristics, the system reduces session preparation time by up to 70% in typical multitrack audio projects. The proposed approach highlights how practical automation, combined with lightweight Neural Network (NN) models, can optimize workflow efficiency in real-world music production environments.https://www.mdpi.com/2078-2489/16/6/494audio engineeringdigital audio workstationsmachine learningaudio workflow optimizationgain stagingaudio classification |
| spellingShingle | Bogdan Moroșanu Marian Negru Georgian Nicolae Horia Sebastian Ioniță Constantin Paleologu A Machine Learning-Assisted Automation System for Optimizing Session Preparation Time in Digital Audio Workstations Information audio engineering digital audio workstations machine learning audio workflow optimization gain staging audio classification |
| title | A Machine Learning-Assisted Automation System for Optimizing Session Preparation Time in Digital Audio Workstations |
| title_full | A Machine Learning-Assisted Automation System for Optimizing Session Preparation Time in Digital Audio Workstations |
| title_fullStr | A Machine Learning-Assisted Automation System for Optimizing Session Preparation Time in Digital Audio Workstations |
| title_full_unstemmed | A Machine Learning-Assisted Automation System for Optimizing Session Preparation Time in Digital Audio Workstations |
| title_short | A Machine Learning-Assisted Automation System for Optimizing Session Preparation Time in Digital Audio Workstations |
| title_sort | machine learning assisted automation system for optimizing session preparation time in digital audio workstations |
| topic | audio engineering digital audio workstations machine learning audio workflow optimization gain staging audio classification |
| url | https://www.mdpi.com/2078-2489/16/6/494 |
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