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A class of random non-stationary signals termed timbre x dynamics isintroduced and studied. These signals are obtained by non-lineartransformations of sta-tionary random gaussian signals, in such a way that thetransformation can be approximated by translations in an appropriaterepresentation domain. In such situations, approximate maximum likelihoodestimation techniques can be de-rived, which yield simultaneous estimation ofthe transformation and the power spectrum of the underlying stationary signal.This paper focuses on the case of modulation and time warping of station-arysignals, and proposes and studies estimation algorithms (based ontime-frequency and time-scale representations respectively) for thesequantities of interest. The proposed approach is validated on numericalsimulations on synthetic signals, and examples on real life car engine sounds.

Time-frequency and time-scale analysis of deformed stationary processes, with application to non-stationary sound modeling