# A Hypothesis Testing Approach to Nonstationary Source Separation

arXiv: https://arxiv.org/abs/2105.06958 · HTML: https://mlsanity.com/paper/2105.06958/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 29 references that are in the map.
- First posted on arXiv 14 May 2021.

## Builds on (most cited first, 1 of 1)

- [Dynamical spectral unmixing of multitemporal hyperspectral images](https://mlsanity.com/paper/1510.04238/) (2015)

## Built on by (most cited first, 1 of 1)

- [Large-Sample Properties of Non-Stationary Source Separation for Gaussian Signals](https://mlsanity.com/paper/2209.10176/) (2022)

## Abstract (arXiv)

The extraction of nonstationary signals from blind and semi-blind multivariate observations is a recurrent problem. Numerous algorithms have been developed for this problem, which are based on the exact or approximate joint diagonalization of second or higher order cumulant matrices/tensors of multichannel data. While a great body of research has been dedicated to joint diagonalization algorithms, the selection of the diagonalized matrix/tensor set remains highly problem-specific. Herein, various methods for nonstationarity identification are reviewed and a new general framework based on hypothesis testing is proposed, which results in a classification/clustering perspective to semi-blind source separation of nonstationary components. The proposed method is applied to noninvasive fetal ECG extraction, as case study.