{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:24:07Z","timestamp":1760059447110,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:00:00Z","timestamp":1749772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/info16060494","type":"journal-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T06:40:27Z","timestamp":1750056027000},"page":"494","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Machine Learning-Assisted Automation System for Optimizing Session Preparation Time in Digital Audio Workstations"],"prefix":"10.3390","volume":"16","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":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7665-139X","authenticated-orcid":false,"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":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7157-5387","authenticated-orcid":false,"given":"Georgian","family":"Nicolae","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7176-6481","authenticated-orcid":false,"given":"Horia Sebastian","family":"Ioni\u021b\u0103","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Information Technology, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,13]]},"reference":[{"key":"ref_1","unstructured":"Kirby, P. (2015). The Evolution and Decline of the Traditional Recording Studio, The University of Liverpool."},{"key":"ref_2","unstructured":"Music Business Worldwide (2025, April 13). Over 100,000 Tracks Are Now Being Uploaded to Streaming Services like Spotify Each Day. Available online: https:\/\/www.musicbusinessworldwide.com\/its-happened-100000-tracks-are-now-being-uploaded\/."},{"key":"ref_3","unstructured":"McGarry, G., Tolmie, P., Benford, S., Greenhalgh, C., and Chamberlain, A. (March, January 25). \u201cThey\u2019re all going out to something weird\u201d Workflow, Legacy and Metadata in the Music Production Process. Proceedings of the ACM Conference on Computer Supported Cooperative Work and Social Computing, Portland, OR, USA."},{"key":"ref_4","unstructured":"U.S. Bureau of Labor Statistics (2025, April 13). Occupational Employment and Wage Statistics: Sound Engineering Technicians, Available online: https:\/\/www.bls.gov\/oes\/current\/oes274014.htm."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Izhaki, R. (2017). Mixing Audio: Concepts, Practices, and Tools, Routledge.","DOI":"10.4324\/9781315716947"},{"key":"ref_6","unstructured":"Jillings, N. (2023). Automating the Production of the Balance Mix in Music Production, Birmingham City University."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1002\/asi.22840","article-title":"The impact of technological advances on recording studio practices","volume":"64","author":"Pras","year":"2013","journal-title":"J. Am. Soc. Inf. Sci. Technol."},{"key":"ref_8","unstructured":"Vanka, S., Safi, M., Roll, J.-B., and Fazekas, G. (2023). Adoption of AI technology in the music mixing workflow: An investigation. arXiv."},{"key":"ref_9","unstructured":"De Man, B., Reiss, J.D., and Stables, R. (2017, January 15). Ten years of automatic mixing. Proceedings of the 3rd Workshop on Intelligent Music Production, Salford, UK."},{"key":"ref_10","first-page":"1","article-title":"A semantic approach to autonomous mixing","volume":"8","author":"Reiss","year":"2013","journal-title":"J. Art Rec. Prod. (JARP)"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2053951718808553","DOI":"10.1177\/2053951718808553","article-title":"Listening without ears: Artificial intelligence in audio mastering","volume":"5","author":"Birtchnell","year":"2018","journal-title":"Big Data Soc."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Moro\u0219anu, B., Negru, M., and Paleologu, C. (2024). Automated Personalized Loudness Control for Multi-Track Recordings. Algorithms, 17.","DOI":"10.3390\/a17060228"},{"key":"ref_13","unstructured":"Harding, P. (2016). Top-Down Mixing\u2014A 12-Step Mixing Program. Mixing Music, Routledge."},{"key":"ref_14","unstructured":"Pestana, P.D., and Reiss, J.D. (2014, January 27\u201329). Intelligent audio production strategies informed by best practices. Proceedings of the AES 53rd International Conference, London, UK."},{"key":"ref_15","unstructured":"De Man, B., Mora-Mcginity, M., Fazekas, G., and Reiss, J.D. (2016, January 13). The open multitrack testbed. Proceedings of the 2nd AES Workshop on Intelligent Music Production, London, UK."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Moffat, D., and Sandler, M.B. (2019). Approaches in intelligent music production. Arts, 8.","DOI":"10.3390\/arts8040125"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Humphrey, E.J., and Bello, J.P. (2014, January 4\u20139). From music audio to chord tablature: Teaching deep convolutional networks to play guitar. Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy.","DOI":"10.1109\/ICASSP.2014.6854952"},{"key":"ref_18","first-page":"1649","article-title":"Acoustic scene classification using convolutional neural network and multiple-width frequency-delta data augmentation","volume":"23","author":"Han","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., and Seybold, B. (2017, January 5\u20139). CNN architectures for large-scale audio classification. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952132"},{"key":"ref_20","unstructured":"Choi, K., Fazekas, G., Sandler, M., and Cho, K. (2017, January 23\u201327). Transfer learning for music classification and regression tasks. Proceedings of the 18th ISMIR Conference, Suzhou, China."},{"key":"ref_21","unstructured":"Pons, J., Nieto, O., Prockup, M., Schmidt, E.M., Ehmann, A.F., and Serra, X. (2018, January 23\u201327). End-to-end learning for music audio tagging at scale. Proceedings of the 19th ISMIR Conference, Paris, France."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ghosal, D., and Kolekar, M.H. (2018). Music genre recognition using deep neural networks and transfer learning. Interspeech, 2087\u20132091.","DOI":"10.21437\/Interspeech.2018-2045"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/TSA.2002.800560","article-title":"Musical genre classification of audio signals","volume":"10","author":"Tzanetakis","year":"2002","journal-title":"IEEE Trans. Speech Audio Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Guinot, J., Quinton, E., and Fazekas, G. (2024). Leave-One-EquiVariant: Alleviating invariance-related information loss in contrastive music representations. arXiv.","DOI":"10.1109\/ICASSP49660.2025.10890270"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hasumi, T., Komatsu, T., and Fujita, Y. (2025). Music Tagging with Classifier Group Chains. arXiv.","DOI":"10.1109\/ICASSP49660.2025.10890872"},{"key":"ref_26","unstructured":"Bogdanov, D., Won, M., Tovstogan, P., Porter, A., and Serra, X. (2019, January 10\u201315). The MTG-Jamendo dataset for automatic music tagging. Proceedings of the Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML), Long Beach, CA, USA."},{"key":"ref_27","unstructured":"Katz, B. (2020). Mastering Audio: The Art and the Science, Focal Press. [3rd ed.]."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"412","DOI":"10.17743\/jaes.2015.0053","article-title":"Intelligent multitrack dynamic range compression","volume":"63","author":"Ma","year":"2015","journal-title":"J. Audio Eng. Soc."},{"key":"ref_29","first-page":"96","article-title":"Autonomous multitrack equalization based on masking reduction","volume":"67","author":"Hafezi","year":"2019","journal-title":"J. Audio Eng. Soc."},{"key":"ref_30","unstructured":"Moffat, D., and Sandler, M.B. (2019, January 16\u201319). Machine learning multitrack gain mixing of drums. Proceedings of the 147th Audio Engineering Society Convention, New York, NY, USA."},{"key":"ref_31","unstructured":"De Man, B., and Reiss, J.D. (2013, January 17\u201320). A knowledge-engineered autonomous mixing system. Proceedings of the 135th Audio Engineering Society Convention, New York, NY, USA."},{"key":"ref_32","unstructured":"Tot, J. (2018). Multitrack Mixing: An Investigation into Music Mixing Practices. [Master\u2019s Thesis, University of York]. Available online: https:\/\/www.york.ac.uk."},{"key":"ref_33","first-page":"17","article-title":"A New Audio Mixing Paradigm: Creative Interaction in Real-Time Remote Collaboration","volume":"15","author":"Stickland","year":"2022","journal-title":"Creat. Ind. J."},{"key":"ref_34","first-page":"1","article-title":"Beyond Skeuomorphism: The Evolution of DAW Interfaces","volume":"13","author":"Bell","year":"2018","journal-title":"J. Art Rec. Prod."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Negru, M., Moro\u015fanu, B., Neac\u015fu, A., Dr\u0103ghicescu, D., and Negrescu, C. (2023, January 25\u201327). Automatic Audio Upmixing Based on Source Separation and Ambient Extraction Algorithms. Proceedings of the 2023 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), Bucharest, Romania.","DOI":"10.1109\/SpeD59241.2023.10314957"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Slizovskaia, O., Kim, L., Haro, G., and Gomez, E. (2019, January 12\u201317). End-to-end sound source separation conditioned on instrument labels. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683800"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","article-title":"Multimodal machine learning: A survey and taxonomy","volume":"41","author":"Baltrusaitis","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Reimers, N., and Gurevych, I. (2019, January 3\u20137). Sentence-BERT: Sentence embeddings using Siamese BERT-networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China.","DOI":"10.18653\/v1\/D19-1410"},{"key":"ref_40","unstructured":"Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., and Ng, A.Y. (July, January 28). Multimodal deep learning. Proceedings of the 28th International Conference on Machine Learning (ICML), Bellevue, WA, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Park, D.S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E.D., and Le, Q.V. (2019, January 15\u201319). SpecAugment: A simple data augmentation method for automatic speech recognition. Proceedings of the Interspeech 2019, Graz, Austria.","DOI":"10.21437\/Interspeech.2019-2680"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","article-title":"A systematic study of the class imbalance problem in convolutional neural networks","volume":"106","author":"Buda","year":"2018","journal-title":"Neural Netw."},{"key":"ref_43","unstructured":"Oramas, S., Nieto, O., Barbieri, F., and Serra, X. (2017, January 23\u201327). Multi-label music genre classification from audio, text, and images using deep features. Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR), Suzhou, China."},{"key":"ref_44","unstructured":"(2011). Algorithms to Measure Audio Programme Loudness and True-Peak Audio Level. Standard No. ITU-R BS.1770-4."},{"key":"ref_45","unstructured":"Ward, D., Reiss, J.D., and Athwal, C. (2012, January 26\u201329). Multitrack Mixing Using a Model of Loudness and Partial Loudness. Proceedings of the AES 133rd Convention, San Francisco, CA, USA."},{"key":"ref_46","unstructured":"Raffel, C., McFee, B., Humphrey, E.J., Salamon, J., Nieto, O., Liang, D., and Ellis, D.P. (2014, January 27\u201331). mir_eval: A transparent implementation of common MIR metrics. Proceedings of the 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/6\/494\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:51:49Z","timestamp":1760032309000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/6\/494"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,13]]},"references-count":46,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["info16060494"],"URL":"https:\/\/doi.org\/10.3390\/info16060494","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2025,6,13]]}}}