Stationary Signal Separation Using Multichannel Local Segmentation

In this work, we study the influence of locally stationary segments as preprocess stage to separate stationary and non-stationary segments. To this, we compare three different segmentation approaches, namely i)cumulative variance based segmentation, ii)PCA based segmentation, and iii)HMM based segmentation. Results are measured as the true and false detection probabilities, and also as the ratio between the real and estimated number of segments. Finally, to achieve the separation, we use the Analytic Stationary Subspace Analysis (ASSA) and results are measured as the correlation between the true and the estimated stationary sources. In this case, we also compare against the best possible ASSA solution. Results show that inclusion of locally stationary segments could enhance or at least achieve optimal estimation of stationary sources.