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Emphasis is placed on present applications and future potential. A detailed examination of music information retrieval, automatic music transcription, music recommendation, and algorithmic composition presents state-of-the-art algorithms and their respective functionalities. The paper underscores recent advancements, including ML-assisted music production and emotion-driven music generation. The survey concludes with a prospective contemplation of future directions of ML within music, highlighting the ongoing growth, novel applications, and anticipation of deeper integration of ML across musical domains. This comprehensive study asserts the profound potential of ML to revolutionize the musical landscape and encourages further exploration and advancement in this emerging interdisciplinary field.<\/jats:p>","DOI":"10.3389\/fnbot.2023.1267561","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T12:35:48Z","timestamp":1699878948000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Harmonizing minds and machines: survey on transformative power of machine learning in music"],"prefix":"10.3389","volume":"17","author":[{"given":"Jing","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.3390\/app8071103","article-title":"An emotion-aware personalized music recommendation system using a convolutional neural networks approach","volume":"8","author":"Abdul","year":"2018","journal-title":"Appl. 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