Spectral Estimation for Multivariate Locally Time-Warped Signals

Spectral estimation generally aims at determining from a single realization of a given signal, the distribution of its power as a function of frequency. In this letter, we focus on multivariate, locally time-warped signals. We show that the spectral estimation problem can also be interpreted as a doubly nonstationary blind source separation (BSS) problem, where both the mixing matrix and the original sources contribute to nonstationarity. We then introduce a BSS algorithm for joint spectral estimation of nonstationary sources. The performance of the proposed approach is evaluated on numerical simulations, and compared with other nonstationary BSS algorithms.