A review on speech separation using NMF and its extensions

Speech separation aims to estimate the target signals produced by individual speech sources from a mixture signal. In this paper, we especially review on data-driven separation methods, where algorithms will be enhanced to produce better dictionary learning which considers the geometric of input data and efficiently performs separation mixture. We review the existing algorithms using non-negative matrix factorization, sparse coding, mixture local dictionary, group lasso, and graph regularization to produce knowledge bases. We also review the extension of NMF by incorporating two state-of-art techniques i.e. bilevel optimization and deep neural network.