Convergence-guaranteed Independent Positive Semidefinite Tensor Analysis Based on Student's t Distribution
This work addresses convergence issues in blind source separation for signal processing applications, representing an incremental improvement over existing IPSDTA methods.
The paper tackles the problem of unstable convergence and limited generative modeling in independent positive semidefinite tensor analysis (IPSDTA) for blind source separation by extending the model to a multivariate Student's t distribution and deriving a new optimization algorithm that guarantees monotonic nonincrease in the cost function, resulting in improved source-separation performance over conventional methods.
In this paper, we address a blind source separation (BSS) problem and propose a new extended framework of independent positive semidefinite tensor analysis (IPSDTA). IPSDTA is a state-of-the-art BSS method that enables us to take interfrequency correlations into account, but the generative model is limited within the multivariate Gaussian distribution and its parameter optimization algorithm does not guarantee stable convergence. To resolve these problems, first, we propose to extend the generative model to a parametric multivariate Student's t distribution that can deal with various types of signal. Secondly, we derive a new parameter optimization algorithm that guarantees the monotonic nonincrease in the cost function, providing stable convergence. Experimental results reveal that the cost function in the conventional IPSDTA does not display monotonically nonincreasing properties. On the other hand, the proposed method guarantees the monotonic nonincrease in the cost function and outperforms the conventional ILRMA and IPSDTA in the source-separation performance.