Source Separation

Source separation aims to decompose complex audio mixtures into their constituent sources, such as individual instruments in a musical recording or speakers in a conversation. Current research emphasizes unsupervised and weakly-supervised approaches, employing diverse architectures including diffusion models, variational autoencoders, and neural networks combined with traditional signal processing techniques like non-negative matrix factorization and beamforming. These advancements are improving the robustness and efficiency of source separation across various applications, from enhancing speech recognition in noisy environments to enabling more sophisticated music generation and analysis.