Time-frequency-masking-based Determined BSS with Application to Sparse IVA
Kohei Yatabe, Daichi Kitamura · 2019
Most of the determined blind source separation (BSS) algorithms related to the independent component analysis (ICA) were derived from mathematical models of source signals. However, such derivation restricts the application of algorithms to explicitly definable source models, i.e., an implicit model associated with some signal-processing procedure cannot be utilized within such framework. In this paper, we propose an extension of the existing algorithm so that any time-frequency masking method (e.g., those developed in speech enhancement literature) can be incorporated into the determined BSS algorithm. As an application of the proposed algorithm, a sparse extension of the well-known independent vector analysis (IVA) is also proposed for illustrating the potentiality of the masking-based implicit source model.