Generating Music Using Machine Learning

This report delves into the realm of music generation using advanced machine learning techniques, focusing on Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and their amalgamation in the form of RNN- GAN. The objective is to explore the capabilities of these models in creating novel and expressive musical compositions. The examines the fundamental concepts of RNNs, known for their prowess in sequence modelling, making them ideal for capturing the temporal intricacies present in music. Additionally, it explores the generative potential of GANs, that employs adversarial training to produce realistic and innovative musical sequences. The synthesis of RNNs and GANs into an integrated RNN-GAN framework is investigated. This hybrid approach aims to leverage the strengths of both architectures, enabling the generation of coherent and diverse musical pieces.