In this article, the authors explore the use of machine learning models for generating music. They explain that music can be thought of as a combination of individual parts, similar to how a musical composition is created by combining different instruments or sounds. The authors discuss various approaches to music generation, including the use of generative adversarial networks (GANs), wavelet transforms, and diffusion models.
To demystify complex concepts, the authors use analogies such as comparing the process of generating music to cooking a meal. They explain that just as a chef combines different ingredients to create a dish, a music generator combines individual parts or sounds to create a musical composition. The authors also highlight the importance of considering the context and aesthetic preferences of the listener when generating music.
The article provides examples of recent advancements in music generation using machine learning models, such as the use of transformers to generate beats and downbeats, and the development of time-frequency transformers for analyzing and manipulating audio signals. The authors also discuss challenges and limitations in music generation, including the need to balance complexity and coherence in the generated music.
Overall, the article aims to provide an overview of the current state of music generation using machine learning models, while also highlighting the potential for future research and applications in this field. By using everyday language and engaging analogies, the authors make the concepts more accessible and easier to understand for a general audience.
Generative Models for Music Composition: A Focus on Context-Aware Audio Generation
