spectral-flatness

Spectral-flatness is computed directly FROM a power spectrum — it is the ratio of geometric to arithmetic mean of spectral power values. Remove the power spectrum and spectral-flatness has no input to operate on; the removal test passes.

Spectral-flatness IS a specific kind of spectral feature: it quantifies how noise-like vs tonal a spectrum is (Glaisher's constant / geometric-to-arithmetic mean ratio). A competent speaker would call spectral-flatness 'a spectral feature.' Nearest kind confirmed.

Which-came-first test: magnitude-spectrum (1960s) predates spectral-flatness (1970s, Widmer). Spectral-flatness operates by computing the ratio of geometric mean to arithmetic mean of the power spectrum values. The magnitude spectrum is the input data that feeds into this computation.

Spectral flatness (Wiener entropy) computes the ratio of geometric mean to arithmetic mean of the magnitude spectrum. Remove magnitude-spectrum and spectral flatness has no input data. The removal test passes.

Spectral-flatness needs spectrum to operate now: it measures the flatness of a spectral density distribution. Remove spectrum and spectral-flatness has no data to analyze — the removal test (Law 8) passes.

Spectral flatness (Wiener entropy) measures the geometric-to-arithmetic mean ratio of the power spectrum, computed from the STFT magnitude. Remove STFT and there is no spectrum to analyze — spectral flatness is inoperable.

Spectral-flatness (the ratio of geometric to arithmetic mean of a power spectrum) is a metric derived from spectral representation. Remove spectral-representation and the concept of spectral-flatness ceases to have meaning. Epoch test confirms: spectral-flatness (0.71) is newer than spectral-representation (0.03).

Spectral whitening is built and maintained for the sake of achieving a flat spectral density, i.e., to remove coloration and produce spectral flatness as a target property for downstream processing. Servant points at master per Law 8d.

Dithering is designed to produce white (spectrally flat) quantization noise — spreading error energy evenly across the frequency spectrum rather than concentrating it as tonal distortion. The purpose of dithering is to achieve spectral flatness of the error signal.