  A new approach for the analysis of nonstationary signals is proposed, with a
focus on audio applications. Following earlier contributions, nonstationarity
is modeled via stationarity-breaking operators acting on Gaussian stationary
random signals. The focus is on time warping and amplitude modulation, and an
approximate maximum-likelihood approach based on suitable approximations in the
wavelet transform domain is developed. This paper provides theoretical analysis
of the approximations, and introduces JEFAS, a corresponding estimation
algorithm. The latter is tested and validated on synthetic as well as real
audio signal.
