A Systematic Survey of Approaches Used in Computer Music Generation

Abstract

Music generation using Artificial Intelligence/
Machine learning (AI/ML) is a rapidly
growing research area that aims to create
algorithms capable of producing musical
compositions autonomously. This paper
provides an overview of the latest
developments in the area of AI/ML-based
music generation. The deep learning models
have demonstrated promise in music
generation, including Recurrent Neural
Networks (RNN), long short-term memory
(LSTM), WaveNet, Generative Pre-trained
Transformer (GPT), and Genetic Algorithms
(GA). A review of the existing research work
on the creation of different music genres such
as classical, jazz, and pop music using these
models is covered. The challenges in music
generation are covered, including the
difficulty in evaluating the quality and
creativity of the generated music along with
the social issues surrounding the ownership
and copyright of the created music. The
development of AI/ML-based music
generation systems has made significant
advances in recent years. The ability to create
an infinite number of original musical
compositions is one of the main benefits of
employing AI/ML algorithms for music
production. Content producers can utilize
these technologies to rapidly and easily create
original soundtracks for their projects, which
can be of special utility to them. Moreover,
the advancement in the music generating
systems could make it possible for more
people to contribute to the creation of music,
regardless of their level of technical or
musical proficiency. As technology develops,
it might also create previously unimagined
opportunities for new forms of musical
expression. The applications include helping
musicians with their work and creating music
for video games and movies.