Enhancing Music Source Separation Using U-Net and Generative Adversarial Network

To achieve enhanced music source separation, the approach of combining the power of a U-Net architecture and Generative Adversarial Networks (GANs) is employed. The proposed system leverages the U-Net architecture for its ability to capture intricate patterns in spectrograms and effectively learn hidden features. Unlike traditional models, the U-Net is designed to process audio information using an encoder-decoder structure, providing a richer representation of the audio scene. Generative Adversarial Network is incorporated to refine the separated sources. The GAN introduces a discriminator network that guides the training process, encouraging the generator (U-Net) to produce source-separated audio that not only matches ground truth sources but also exhibits high-fidelity and perceptual quality. This paper aims to address common challenges in music source separation, such as dealing with overlapping sources and preserving the quality of the separated signals. By combining the strengths of the U-Net and GAN, this system stands at par with the state-of-the-art methods in music source separation, offering a promising solution for applications in audio restoration and immersive audio experiences.