{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T12:05:02Z","timestamp":1782389102338,"version":"3.54.5"},"reference-count":29,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T00:00:00Z","timestamp":1716508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006595","name":"Ministry of Research, Innovation and Digitization, CNCS-UEFISCDI","doi-asserted-by":"publisher","award":["PN-III-P4-PCE-2021-0780"],"award-info":[{"award-number":["PN-III-P4-PCE-2021-0780"]}],"id":[{"id":"10.13039\/501100006595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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 \u2018virtual assistant\u2019 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.<\/jats:p>","DOI":"10.3390\/a17060228","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T05:36:06Z","timestamp":1716528966000},"page":"228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automated Personalized Loudness Control for Multi-Track Recordings"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7416-1161","authenticated-orcid":false,"given":"Bogdan","family":"Moro\u0219anu","sequence":"first","affiliation":[{"name":"Faculty of Electronics, Telecommunications, and Information Technology, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marian","family":"Negru","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Telecommunications, and Information Technology, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0379-2360","authenticated-orcid":false,"given":"Constantin","family":"Paleologu","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Telecommunications, and Information Technology, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,24]]},"reference":[{"key":"ref_1","unstructured":"De Man, B. 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Adam: A method for stochastic optimization. arXiv."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/6\/228\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:47:56Z","timestamp":1760107676000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/6\/228"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,24]]},"references-count":29,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["a17060228"],"URL":"https:\/\/doi.org\/10.3390\/a17060228","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,24]]}}}