# Time frequency analysis

Multiplying your exposure to uncertainty principles

January 6, 2018 — December 21, 2021

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The approximation of a non-stationary signal by many locally stationary signals, which is a thing we might do in an analysis or synthesis procedure. One of the many places where uncertainty principles come into play. If we let the window size shrink to a single sample, then we are looking instead at empirical mode decompositions.

Chromatic derivatives, Welch-style DTFT spectrograms, wavelets sometimes. Wigner distribution (which is sort-of a joint distribution over time and frequency). Constant Q transforms.

Much to learn here, even in the deterministic case.

I am especially interested in the Bayesian approach to this, a.k.a. probabilistic spectral analysis, which treats this as a problem in random functions.

TODO: In the classical setup we might still talk about *distributions* although these are usually *Wigner distributions* not probability distribution, which quantify something related to time-frequency uncertainty rather than posterior likelihoods. I would like to understand that.

## 1 Effect of windows

## 2 Adaptive windows

The Adaptspec methods (Bertolacci et al. 2020; Rosen, Wood, and Stoffer 2012) assign a probabilisty distribution to possible locally stationary windows converting this into a probabilistic spectral problems. Without an explicit spectrogram, so does Saatçi, Turner, and Rasmussen (2010).

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