Underdetermined blind source separation using CapsNet

In this paper, we consider the problem of separating the speech source signal from the underdetermined convolutive mixture signals using capsule network (CapsNet). The objective of this paper is twofold. They are (1) to improve the underdetermined convolutive blind source separation algorithm in terms of signal-to-distortion ratio, signal-to-interference ratio and signal-to-artifact ratio; (2) to minimize the computational burden of the algorithm so that it is useful for applications like speech recognition system. The time–frequency points of the observed mixture signals are input to the first layer of CapsNet. In the first layer, single-source active point (SSP) is calculated using the ratio of mixtures. These SSPs are lower-level capsules in our system. In the second layer, we find a cluster center using a dynamic routing algorithm and these clusters are used to construct a binary mask. Finally, the algorithm solves the permutation problem by determining the correlation between the amplitudes of adjacent frequency bins. We test our algorithm on the live recording mixture signals obtained in the real environment and synthetically convoluted mixture signals. The test result shows the effectiveness of the proposed method when compared with the existing algorithms in terms of computational load, signal-to-distortion ratio and signal-to-interference ratio.