Analyzing principles and applications of machine learning in music: Emotion music generation, and style modeling

Abstract

The combination of machine learning with music composition and production is proving viable for innovative applications, enabling the creation of novel musical experiences that were once the exclusive domain of human composers. This paper explores the transformative role of machine learning in music, particularly focusing on emotion music generation and style modeling. Through the development and application of models including DNNs, GANs, and Autoencoders, this study delves into how machine learning is being harnessed to not only generate music that embodies specific emotional contexts but also to transfer distinct musical styles onto new compositions. This research discusses the principles of these models, their operational mechanisms, and evaluates their effectiveness through various metrics such as accuracy, precision, and creative authenticity. The outcomes illustrate that these technologies not only enhance the creative possibilities in music but also democratize music production, making it more accessible to non-experts. The implications of these advancements suggest a significant shift in the music industry, where machine learning could become a central component of creative processes. These results pave a path to the understanding of the potential and limitations of machine learning in music and forecasts future trends in this evolving landscape.