Joshua Min

Music Generation using Machine Learning

Started Feb. 6, 2024

Abstract or project description

Generating music using machine learning involves training algorithms on large datasets of music to learn the underlying patterns and structures characteristic of different genres or styles. We start by collecting and preprocessing a diverse set of musical pieces, which may include melodies, harmonies, rhythms, and timbres. Subsequently, the machine learning model, often a deep neural network, is trained on this data, learning to predict or generate new musical sequences that are stylistically similar to the input. By adjusting parameters and refining the model, we can enhance its ability to create music that is both novel and aesthetically pleasing. In this work, we explore the application of various architectures such as recurrent neural networks (RNNs) and generative adversarial networks (GANs) for this purpose. This suggests that machine learning can become a relevant tool in the creative process, providing new avenues for musical expression and experimentation.