Real-Time Independent Vector Analysis with a Deep-Learning-Based Source Model

Abstract

In this paper, we present a real-time blind source separation (BSS) algorithm, which unifies the independent vector analysis (IVA) as a spatial model and a deep neural network (DNN) as a source model. The auxiliary-function based IVA (Aux-IVA) is utilized to update the demixing matrix, and the required time-varying variance of the speech source is estimated by a DNN. The DNN could provide a more accurate source model, which then helps to optimize the spatial model. In addition, because the DNN is used to estimate the source variance instead of the source power spectrogram, the size of DNN can be reduced significantly. Experiment results show that the joint utilization of the model-based approach and the data-driven approach provides a more efficient solution than just alone in terms of convergence rate and source separation performance.