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

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          Abstract

          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.

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          Most cited references111

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          Deep learning in neural networks: An overview

          In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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            Machine learning: Trends, perspectives, and prospects.

            Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
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              Generative Adversarial Networks: An Overview

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                Author and article information

                Contributors
                URI : http://loop.frontiersin.org/people/2390836/overviewRole: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role: Role:
                Journal
                Front Neurorobot
                Front Neurorobot
                Front. Neurorobot.
                Frontiers in Neurorobotics
                Frontiers Media S.A.
                1662-5218
                10 November 2023
                2023
                : 17
                : 1267561
                Affiliations
                Department of Music, Zhumadian Preschool Education College , Henan, China
                Author notes

                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@ 123456126.com
                Article
                10.3389/fnbot.2023.1267561
                10668594
                e06994d1-14e2-412c-9a9d-a8077bd4e59e
                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.

                History
                : 26 July 2023
                : 16 October 2023
                Page count
                Figures: 0, Tables: 5, Equations: 0, References: 117, Pages: 15, Words: 13096
                Funding
                The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
                Categories
                Neuroscience
                Review

                Robotics
                machine learning,automatic music,music recommendation,music classification,music generation,music transcription,applications

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