Speaker Separation

Speaker separation aims to isolate individual voices from a mixture of sounds, a crucial task for applications like speech recognition in noisy environments and virtual meetings. Current research focuses on developing robust deep learning models, including neural networks employing attention mechanisms, and those integrating audio and visual information, to handle multiple speakers, reverberation, and missing data. These advancements leverage techniques like complex spectral mapping, spatial activity analysis, and speaker embeddings to improve separation accuracy and efficiency, impacting fields ranging from assistive hearing technologies to music information retrieval.