Journal Article

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

Liang, Jing

2023

Overview

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.

• Publisher: Frontiers Research Foundation,Frontiers Media S.A
• Subject: Algorithms / Artificial intelligence / automatic music / Classification / Cognition & reasoning / Deep learning / Emotions / Harmony (Music) / Information processing / Information retrieval / Innovations / Learning algorithms / Listening / Machine learning / music classification / music generation / music recommendation / music transcription / Musical performances / Neural networks / R&D / Recommender systems / Research & development / Surveys