Separation Model

Speech separation, aiming to isolate individual sound sources from a mixture, is a crucial area of research with applications in hearing aids, teleconferencing, and music production. Current efforts focus on improving the generalization of separation models to diverse real-world acoustic conditions, employing techniques like self-supervised learning and novel architectures such as conformers and state-space models to enhance performance and efficiency. These advancements address limitations of existing methods, particularly in handling noisy, reverberant environments and an unknown number of sources, leading to more robust and practical sound separation systems.