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