Spectral estimation for non-stationary signal classes

International audienceAn approach to the spectral estimation for some classes of non-stationary random signals is developed, that addresses stationary random processes deformed by a stationarity-breaking transformation. Examples include frequency modulation , time warping, non-stationary filtering and others. Under suitable smoothness assumptions on the transformation, approximate expressions are obtained in adapted representation spaces. In the Gaussian case, this leads to approximate maximum likelihood estimation algorithms, which are illustrated on synthetic as well as real signals