Local maxima of white noise spectrograms and Gaussian Entire Functions

TL;DR

This paper confirms theoretical predictions about the distribution and density of local maxima in white noise spectrograms modeled by Gaussian Entire Functions, providing detailed statistical and geometric insights.

Contribution

It establishes the expected density of critical points and local maxima in Gaussian spectrograms, linking them to Gaussian Entire Functions and complex geometric structures.

Findings

Expected density of 5/3 critical points per unit area

One-third of critical points are local maxima

Distribution of heights of spectrogram extrema computed

Abstract

We confirm Flandrin's prediction for the expected average of local maxima of spectrograms of complex white noise with Gaussian windows (Gaussian spectrograms or, equivalently, modulus of weighted Gaussian Entire Functions), a consequence of the conjectured double honeycomb mean model for the network of zeros and local maxima, where the area of local maxima centered hexagons is three times larger than the area of zero centered hexagons. More precisely, we show that Gaussian spectrograms, normalized such that their expected density of zeros is 1, have an expected density of 5/3 critical points, among those 1/3 are local maxima, and 4/3 saddle points, and compute the distributions of ordinate values (heights) for spectrogram local extrema. This is done by first writing the spectrograms in terms of Gaussian Entire Functions (GEFs). The extrema are considered under the translation invariant derivative of the Fock space (which in this case coincides with the Chern connection from complex differential geometry). We also observe that the critical points of a GEF are precisely the zeros of a Gaussian random function in the first higher Landau level. We discuss natural extensions of these Gaussian random functions: Gaussian Weyl-Heisenberg functions and Gaussian bi-entire functions. The paper also contains a bibliographic review of recent results on the theory and applications of white noise spectrograms, connections between several developments, and is partially intended as a pedestrian introduction to the topic.