Robust chirp parameter estimation for Hann windowed signals

Abstract

The sinusoidal model has been a fundamentally important signal representation for coding and analysis of audio. We present an enhancement to sinusoidal modeling in the form of a linear frequency chirp parameter estimator applicable to Hann-windowed quasi-sinusoidal signals. The estimator relies on models of the phase curvature and peak width of a given chirp signal's FFT magnitude domain peak. We show that different models are applicable for smaller and larger values of the chirp parameter, derived respectively from Taylor series and Fresnel integral analysis of the signal. We construct an estimator for the transition region between the two models via a neural net. Results indicate that the estimator is robust to noise and outperforms any known chirp parameter estimators for Hann windowed signals.