A Tutorial on AI Music Composition

Abstract

AI music composition is one of the most attractive and important topics in artificial intelligence, music, and multimedia. The typical tasks in AI music composition include melody generation, song writing, accompaniment generation, arrangement, performance generation, timbre rendering, sound generation, and singing voice synthesis, which cover different modalities (e.g., symbolic music score, sound) and well match to the theme of ACM Multimedia. As the rapid development of artificial intelligence techniques such as content creation and deep learning, AI based music composition has achieved rapid progress, but still encountered a lot of challenges. A thorough introduction and review on the basics, the research progress, as well as how to address the challenges in AI music composition are timely and necessary for a broad audience working on artificial intelligence, music, and multimedia. In this tutorial, we will first introduce the background of AI music composition, including music basics and deep learning techniques for music composition. Then we will introduce AI music composition from two perspectives: 1) key components, which include music score generation, music performance generation, and music sound generation; 2) advanced topics, which include music structure/form/style/emotion modeling, timbre synthesis/transfer/mixing, etc. At last, we will point out some research challenges and future directions in AI music composition. This tutorial can serve both academic researchers and industry practitioners working on AI music composition.

A Comprehensive Overview of AI-Enabled Music Classification and Its Influence in Games

“…[1] AI and music [2] music and AI [3] Music creation by example [4] AI and music: open questions of copyright law and engineering praxis [5] Amusic composition based on AI [6] Novice-AI music co-creation [7] A Tutorial on AI Music Composition [8] Mind Band [9] AI can change/improve/influence music [10] On the role of artificial intelligence in music research [11] AI-based intelligent music [12] Sleep pattern analysis and improvement by AI and music therapy [13] AI, ML, and music [14] Music representation [15] AI to music composition [16] Music composition using AI [17] Arts, computers, and AI [18] Music theory, the missing link between music-related big data and AI [19] AI approaches for automated melody generation [20] Ants can play music [21] Sound and music in games [22] procedural music in video games [23] music in educational games [24] music matters [25] Generative music in video games [26] Flipped learning as a new educational paradigm [27] barelymusician [28] Playing with sound [29] Effects of built-in audio versus unrelated background music [30] grounded theory of music use [31] AI & popular music [32] Cross-industry innovation [33] Music to middleware [34] Hear the music, play the Game [35] Music Everywhere [36] blockchain on the music industry [37] Music arena game [38] video game music genre...…”

The Style Transformation of Gu-Zheng Music Based on the Improved Model of Cyclegan

“…The generative adversarial network is a deep learning model, a class of implicit generative models proposed by [19]. The model generates high quality output by playing the two modules (generative and discriminative) of the framework against each other.…”

Long Short-Term Memory-Based Music Analysis System for Music Therapy

“…In addition, a significant issue of composition frustration of music can be solved by the proposed system. Tan and Li (2021) discussed deep learning-based algorithms for music generation. They presented how to use different deep learning models to composite music and melodies.…”