Abstract
This paper provides a way to classify vocal disorders for clinical applications. This goal is achieved by means of geometric signal separation in a feature space. Typical quantities from chaos theory (like entropy, correlation dimension and first lyapunov exponent) and some conventional ones (like autocorrelation and spectral factor) are analysed and evaluated, in order to provide entries for the feature vectors. A way of quantifying the amount of disorder is proposed by means of a healthy index that measures the distance of a voice sample from the centre of mass of both healthy and sick clusters in the feature space. A successful application of the geometrical signal separation is reported, concerning distinction between normal and disordered phonation.
Highlights
The voicing source can produce in addition to periodic vibrations a great variety of complex signals, due to the fact that many sources of non-linearity are involved in the air-flow production and in the laryngeal vibration processes [1]
Many studies [1,4] suggest that some of the complexities observed in rough, disordered voices are not caused by external sources, but by the intrinsic nonlinear dynamics of vocal fold movement
Given the nonlinearities associated with the laryngeal source, this should not come as a great surprise
Summary
The voicing source can produce in addition to periodic vibrations (the normal phonation) a great variety of complex signals, due to the fact that many sources of non-linearity are involved in the air-flow production and in the laryngeal vibration processes [1]. These features have been modelled successfully in the past years by an asymmetric two-mass model of the vocal folds [2]. Many studies [1,4] suggest that some of the complexities observed in rough, disordered voices are not caused by external sources, but by the intrinsic nonlinear dynamics of vocal fold movement. Given the nonlinearities associated with the laryngeal source (i.e. the pressure-flow relation in the glottis, the stress-strain curves of vocal fold tissues, the vocal fold collisions), this should not come as a great surprise
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