Determined BSS by Combination of IVA and DNN via Proximal Average

Abstract

This paper proposes a novel approach for determined blind source separation (BSS) assisted by deep neural network (DNN). Determined BSS algorithms, including independent vector analysis (IVA), separate source signals from multi-channel mixtures by estimating demixing filters according to their source models. Our method realizes a combined source model based on IVA and DNN in terms of proximal average. This combination allows our BSS algorithm to incorporate a single-channel denoising DNN without any specialized architecture/training, and hence the proposed method can circumvent the difficulty of training a DNN tailored for a determined BSS algorithm. Our experimental results show that the proposed method can stably and consistently improve the separation performance by combining a DNN trained with a denoising task.