Mixture Signal

Mixture signal analysis focuses on separating individual sources from overlapping signals, a crucial task across diverse fields like audio processing, biomedical signal analysis, and geophysics. Current research heavily utilizes deep learning, particularly employing neural networks such as encoder-decoders, conformers, and variational autoencoders, often incorporating probabilistic frameworks and leveraging multi-channel information or spatial characteristics to enhance source separation. These advancements improve accuracy and efficiency in applications ranging from speech enhancement and vital sign monitoring to genomic sequence classification and planetary data analysis. The development of robust and computationally efficient methods for mixture signal separation continues to be a significant area of investigation with broad practical implications.