A Novel Approach Based on Voronoï Cells to Classify Spectrogram Zeros of Multicomponent Signals

In this paper, we propose a novel approach to classify the spectrogram zeros (SZs) of multicomponent signals based on the analysis of the Voronoï cells associated with these zeros. More precisely, the characterization of the distribution of the spectrogram maxima of a complex white Gaussian noise along the edges of the Voronoï cells associated with SZs enables us to derive an algorithm to classify the different types of zeros present in the spectrogram of a multicomponent signal. Numerical applications on simulated signals confirm the relevance of the proposed classification algorithm, and an illustration on a real signal concludes the paper.