Given an astrophysical observation with an arbitrary carrier frequency and an unknown scale under an additive white noise, s’(t)≡s(αt)+n(t), its wavelet transform is W’(a,b)≡(s’(t),${\mathit{h}}_{\mathit{a}\mathit{b}}$(t)), as computed by the inner product with a daughter wavelet ${\mathit{h}}_{\mathit{a}\mathit{b}}$(t)≡h((t-b)/a)/a. W’(a,b) equals the original transform W(a,b)≡(s(t),${\mathit{h}}_{\mathit{a}\mathit{b}}$(t)) displaced along the radial direction W’(a,b)=W(αa,αb) plus noise in the time-scale joint-representation plane. A bank of wedge-shaped detectors collects those displaced transforms W’(a,b) to create a set of invariant features. These features are fed into a two-layer feed-forward artificial neural network, to interpolate discrete sampling, as demonstrated successfully for real-time-signal automatic classification. Useful wavelet applications in turbulence onset, spectrum analyses, fractal aggregates, and bubble-chamber particle-track pattern-recognition problems are indicated but are modeled, in the interest of simplicity, in a one-dimensional example.

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