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Harmonizing minds and machines: survey on transformative power of machine learning in music
This survey explores the symbiotic relationship between Machine Learning (ML) and music, focusing on the transformative role of Artificial Intelligence (AI) in the musical sphere. Beginning with a historical contextualization of the intertwined trajectories of music and technology, the paper discuss...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668594/ https://www.ncbi.nlm.nih.gov/pubmed/38023456 http://dx.doi.org/10.3389/fnbot.2023.1267561 |
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author | Liang, Jing |
author_facet | Liang, Jing |
author_sort | Liang, Jing |
collection | PubMed |
description | This survey explores the symbiotic relationship between Machine Learning (ML) and music, focusing on the transformative role of Artificial Intelligence (AI) in the musical sphere. Beginning with a historical contextualization of the intertwined trajectories of music and technology, the paper discusses the progressive use of ML in music analysis and creation. 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. |
format | Online Article Text |
id | pubmed-10668594 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106685942023-01-01 Harmonizing minds and machines: survey on transformative power of machine learning in music Liang, Jing Front Neurorobot Neuroscience This survey explores the symbiotic relationship between Machine Learning (ML) and music, focusing on the transformative role of Artificial Intelligence (AI) in the musical sphere. Beginning with a historical contextualization of the intertwined trajectories of music and technology, the paper discusses the progressive use of ML in music analysis and creation. 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. Frontiers Media S.A. 2023-11-10 /pmc/articles/PMC10668594/ /pubmed/38023456 http://dx.doi.org/10.3389/fnbot.2023.1267561 Text en Copyright © 2023 Liang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Liang, Jing Harmonizing minds and machines: survey on transformative power of machine learning in music |
title | Harmonizing minds and machines: survey on transformative power of machine learning in music |
title_full | Harmonizing minds and machines: survey on transformative power of machine learning in music |
title_fullStr | Harmonizing minds and machines: survey on transformative power of machine learning in music |
title_full_unstemmed | Harmonizing minds and machines: survey on transformative power of machine learning in music |
title_short | Harmonizing minds and machines: survey on transformative power of machine learning in music |
title_sort | harmonizing minds and machines: survey on transformative power of machine learning in music |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668594/ https://www.ncbi.nlm.nih.gov/pubmed/38023456 http://dx.doi.org/10.3389/fnbot.2023.1267561 |
work_keys_str_mv | AT liangjing harmonizingmindsandmachinessurveyontransformativepowerofmachinelearninginmusic |