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.

Voice health is traditionally assessed by methods that rely on the perception of a clinician, who integrates auditory and visual cues in order to reach a conclusion about the voice under evaluation. However, these tasks suffer from inter-professional variability due to its subjective nature, which is why more objective, computational-based methods are of interest. Two examples of such subjective tasks are the classification of voices in three types according to their periodicity, also termed voice typing, and the evaluation of six aspects of voice quality by means of the consensus auditory-perceptual evaluation of voice (CAPE-V) protocol. In this paper, two approaches to emulate each of those tasks are introduced, based on simple features extracted from the second-order scattering transform coefficients and support vector machines. Firstly, a system for automatic voice typing was trained and its classification performance was evaluated for intra and inter-dataset trials using two widely known corpora. Accuracies above 80%, comparable to the state-of-the-art, were found for all the experiments conducted. Secondly, a multidimensional, multioutput regression chain model was used to automatically grade the voice quality features of the CAPE-V protocol, obtaining errors and correlation coefficients that are comparable to those found for three human raters.

Voice jitter is defined as a random perturbation of the glottal cycle duration which can be useful for voice parametrization and that usually depends on finding fiducial points in this signal. In this paper, a novel application of the Fourier-based high-order synchrosqueezing (FSSTN) operators on voice signals is introduced for voice jitter estimation without period-segmentation. To this end, an innovative interpretation of the relative jitter formula in terms of the total variation of the sequence of periods is proposed. This allows us to derive an algorithm for jitter estimation that uses the (continuous) instantaneous fundamental frequency of the signal and its first derivative (chirp rate) which can be obtained, respectively, from the local complex frequency and the local complex modulation FSSTN operators. Numerical experiments using synthetic signals with known true jitter show that this novel approach yields similar results to other state-of-the-art method, PRAAT, for true jitter within the range [0.2%, 1.2%], and that it outperforms PRAAT for true jitter values in the range [1%, 15%]. The here proposed method seems a promising tool for voice jitter estimation and constitutes a novel application of the high-order synchrosqueezing operators for voice signals with potential impact on jitter modeling and on the clinical field.