Music Source Separation

Music source separation aims to isolate individual instruments or vocals from a mixed audio recording, a challenging task due to the complex interplay of sounds. Current research focuses on developing robust and efficient models, employing architectures like transformers, recurrent neural networks, and diffusion models, often incorporating multi-band processing and spatial information to improve accuracy. These advancements are significant for improving music production workflows, enabling new creative possibilities, and advancing audio signal processing techniques more broadly. Furthermore, the development of large, high-quality datasets and novel training strategies, including self-supervised and few-shot learning, are key to pushing the field forward.