  We develop a timescale synthesis-based probabilistic approach for the
modeling of locally stationary signals. Inspired by our previous work, the
model involves zero-mean, complex Gaussian wavelet coefficients, whose
distribution varies as a function of time by time dependent translations on the
scale axis. In a maximum a posteriori approach, we propose an estimator for the
model parameters, namely the time-varying scale translation and an underlying
power spectrum. The proposed approach is illustrated on a denoising example. It
is also shown that the model can handle locally stationary signals with fast
frequency variations, and provide in this case very sharp timescale
representations more concentrated than synchrosqueezed or reassigned wavelet
transform.
