Harmonizing minds and machines - survey on transformative power of machine learning in music

Aug 5, 2026

This review investigates the symbiotic relationship between machine learning and music, emphasizing how artificial intelligence is reshaping analysis and creative workflows. It traces historical connections between music and technology and reviews modern uses of ML across music information retrieval, automatic music transcription, music recommendation, and algorithmic composition. It highlights state-of-the-art methods, including ML-assisted music production and emotion-driven music generation, and closes by outlining future research directions and deeper integration prospects.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Machine learning as a new technological driver in music

Factors shaping music impact and cognitive interpretations

Selecting ML technologies by task and resources

Core ML applications in music

Music information retrieval

Automatic music transcription

Music recommendation

Algorithmic composition

Advances and future directions

TYPE Review PUBLISHED 10 November 2023 DOI 10. 3389/fnbot.2023.1267561 OPEN ACCESS EDITED BY Surjo R. Soekadar, Charité University Medicine Berlin, Germany REVIEWED BY Adam Safron, Johns Hopkins University, United States Charles Courchaine, National University, United States *CORRESPONDENCE Jing Liang [liangjing7708@126.com](liangjing7708@126.com) RECEIVED 26 July 2023 ACCEPTED 16 October 2023 PUBLISHED 10 November 2023 CITATION Liang J (2023) Harmonizing minds and machines: survey on transformative power of machine learning in music. Front. Neurorobot. 17:1267561 . doi: 10.3389/fnbot.2023.1267561 COPYRIGHT © 2023 Liang. 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. Harmonizing minds and machines: survey on transformative power of machine learning in music Jing Liang* Department of Music, Zhumadian Preschool Education College, Henan, China 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. KEYWORDS machine learning, automatic music, music recommendation, music classiﬁcation, music generation, music transcription, applications 1. Introduction Music, a universal language transcending cultural and linguistic barriers, has always been fertile ground for the advent and progression of technology. From the 􀀂rst simple mechanical devices that created sound to the sophisticated digital platforms enabling global music streaming, technology has continuously reshaped the music landscape (Cross, 2001) . The most recent and promising addition to this technological symphony is machine learning, a revolutionary 􀀂eld within arti􀀂cial intelligence. Machine Learning (ML), characterized by its ability to learn and improve from experience without explicit programming (Zhou, 2021), is in􀀂ltrating various domains, pushing the boundaries of innovation and traditional paradigms. A harmonious and transformative synergy is born when it encounters the expansive realm of music. As it ventures into music information retrieval, automatic music transcription, music recommendation, and algorithmic composition, ML leaves an indelible footprint on the musical world (Briot et al., 2020) . Furthermore, the authors (Safron, 2020) proposed using the Free Energy Principle and Active Inference Framework (FEP-AI) to integrate leading theories of consciousness with ML approaches to modeling the brain. The authors argue that this integration can help bridge the gap between cognitive science and AI research and lead to a mor

FAQs

What relationship does the survey focus on between machine learning and music?

It examines the mutually reinforcing connections between machine learning and music, highlighting how ML transforms musical analysis and music creation through AI-enabled techniques.

Which main tasks in music does the review cover?

The review covers music information retrieval, automatic music transcription, music recommendation, and algorithmic composition, discussing algorithms and their roles.

What future directions does the survey suggest for machine learning in music?

It concludes with a forward-looking discussion of upcoming directions, emphasizing continued growth, novel applications, and deeper integration of ML across musical domains.

Aug 5, 2026

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