New paper submitted: Spectral analysis of stationary random bivariate signals

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Together with Nicolas Le Bihan and Pierre Chainais, we have arXived a novel paper entitled Spectral analysis of stationary random bivariate signals.

Here is the abstract:

A novel approach towards the spectral analysis of stationary random bivariate signals is proposed. Using the Quaternion Fourier Transform, we introduce a quaternion-valued spectral representation of random bivariate signals seen as complex-valued sequences. This makes possible the definition of a scalar quaternion-valued spectral density for bivariate signals. This spectral density can be meaningfully interpreted in terms of frequency-dependent polarization attributes. A natural decomposition of any random bivariate signal in terms of unpolarized and polarized components is introduced. Nonparametric spectral density estimation is investigated, and we introduce the polarization periodogram of a random bivariate signal. Numerical experiments support our theoretical analysis, illustrating the relevance of the approach on synthetic data.

The proposed tools will be soon available as part of BiSPy, stay tuned!