IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 13, No. 4, December 2024, pp. 3715~3726 ISSN: 2252-8938, DOI: 10.11591/ijai.v13.i4.pp3715-3726
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Application of artificial intelligence in music generation: a systematic review Frank Manuel Gutiérrez Paitan, María Acuña Meléndez, Christian Ovalle Departmento de Ingenieria, Universidad Tecnológica del Perú, Lima, Perú
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ABSTRACT
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Our analysis explores the benefits of artificial intelligence (AI) in music generation, showcasing progress in electronic music, automatic music generation, evolution in music, contributions to music-related disciplines, specific studies, contributions to the renewal of western music, and hardware development and educational applications. The identified methods encompass neural networks, automation and simulation, neuroscience techniques, optimization algorithms, data analysis, and Bayesian models, computational algorithms, and music processing and audio analysis. These approaches signify the complexity and versatility of AI in music creation. The interdisciplinary impact is evident, extending into sound engineering, music therapy, and cognitive neuroscience. Robust frameworks for evaluation include Bayesian models, fractal metrics, and the statistical creator-evaluator. The global reach of this research underscores AI's transformative role in contemporary music, opening avenues for future interdisciplinary exploration and algorithmic enhancements.
Received Jan 3, 2024 Revised Feb 29, 2024 Accepted Mar 21, 2024 Keywords: Artificial intelligence Automation and simulation Music generation Music processing Optimization algorithms Systematic review
This is an open access article under the CC BY-SA license.
Corresponding Author: Christian Ovalle Departmento de Ingenieria, Universidad Tecnológica del Perú Lima, Perú Email: dovalle@utp.edu.pe
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INTRODUCTION In recent times, there has been an increase in interest and development in the field of music creation [1]. Music is a fundamental part of human culture that has undergone a significant evolution over the centuries, adapting to different cultures, styles, and technologies [2]. In view of this, we must bear in mind that the complex task of generating musical chords has been tackled by different methods throughout history [3]. In addition, the combination of music theory with computational algorithms has made possible the creation of more accurate and adaptable methods of chord generation [4]. During the 1990s, approaches based on artificial intelligence (AI), such as expert systems and neural networks, have emerged [5]. In addition, the wide variety of musical styles has generated a demand for methods to generate chords that are flexible and capable of addressing diverse genres [6], therefore, the initial methods for generating chords were based on rules derived from musical knowledge [7]. In view of this, the generation of music through models has undergone a paradigm shift with advances in AI and machine learning (ML) [8]. These approaches were basically simple and had restrictions in terms of the diversity and expressiveness of the chords that they could produce [9], bearing in mind that, in the most recent decade, approaches that rely on ML, such as genetic algorithms and deep neural networks, have emerged [10]. Also, the use of deep learning strategies to produce a variety of content (sound track, tracks, and bases) is on the increase [11], in view of this, the incorporation of AI methods has radically transformed the production of chords, making possible the automatic generation of complicated harmonic sequences [8]. In particular the interactive creation of chords, Journal homepage: http://ijai.iaescore.com