Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection

Background: Voice disorders affect patients profoundly, and acoustic tools can potentially measure voice function objectively. Disordered sustained vowels exhibit wide-ranging phenomena, from nearly periodic to highly complex, aperiodic vibrations, and increased "breathiness". Modelling and surrogate data studies have shown significant nonlinear and non-Gaussian random properties in these sounds. Nonetheless, existing tools are limited to analysing voices displaying near periodicity, and do not account for this inherent biophysical nonlinearity and non-Gaussian randomness, often using linear signal processing methods insensitive to these properties. They do not directly measure the two main biophysical symptoms of disorder: complex nonlinear aperiodicity, and turbulent, aeroacoustic, non-Gaussian randomness. Often these tools cannot be applied to more severe disordered voices, limiting their clinical usefulness.

Methods: This paper introduces two new tools to speech analysis: recurrence and fractal scaling, which overcome the range limitations of existing tools by addressing directly these two symptoms of disorder, together reproducing a "hoarseness" diagram. A simple bootstrapped classifier then uses these two features to distinguish normal from disordered voices.

Results: On a large database of subjects with a wide variety of voice disorders, these new techniques can distinguish normal from disordered cases, using quadratic discriminant analysis, to overall correct classification performance of 91.8% plus or minus 2.0%. The true positive classification performance is 95.4% plus or minus 3.2%, and the true negative performance is 91.5% plus or minus 2.3% (95% confidence). This is shown to outperform all combinations of the most popular classical tools.

Conclusions: Given the very large number of arbitrary parameters and computational complexity of existing techniques, these new techniques are far simpler and yet achieve clinically useful classification performance using only a basic classification technique. They do so by exploiting the inherent nonlinearity and turbulent randomness in disordered voice signals. They are widely applicable to the whole range of disordered voice phenomena by design. These new measures could therefore be used for a variety of practical clinical purposes.
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Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy

Models and Analysis of Vocal Emissions for Biomedical Applications

The Models and Analysis of Vocal Emissions with Biomedical Applications (MAVEBA) workshop came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy

Numerical study of the vibrations of an elastic container filled with an inviscid fluid

International audienceWe investigate numerically the vibrational behavior of a simple finite element model that stands for an elastic container filled with an inviscid fluid. The underlying mathematical model is detailed and its spectra is characterized. The finite element method relies upon the added-mass formulation of Morand and Ohayon. A parametric study allows to characterize the system's response to dimensionless parameters in terms of eigenfrequencies. Then an insight into the mode shapes is provided, with a discussion on the presence and the behavior of singular modes caused by specific boundary conditions in the fluid

Model Reduction of Muscle-Driven Tissue Models

Biomechanical simulations are a necessary tool for a proper understanding of biomechanics and hence are subject to intense research. One field that relies on this research is articulatory speech synthesis as it attempts to simulate the physics of the speech production process. Out of the many aspects involved, muscle driven tissue is one of the most important as it is required to simulate the deformable structures of the vocal tract. Modelling of muscle driven tissue requires continuum models of high complexity for the purpose of accuracy. On the other hand, time-efficient models are desirable in order to provide fast simulations which enable the user to test input parameters interactively. These requirements impose limitations on each other as the time-efficiency of a model is reduced with increasing complexity, hence techniques that can bridge the gap between these requirements are needed. This thesis attempts to bridge this gap through two major contributions. Model reduction techniques, that up until now have only been applied to inactive materials, have been implemented and tested for muscle driven tissue models. The implementation has been made in a general way to ensure that it can be used for biomechanical simulations in other fields than articulatory speech synthesis. In addition, the implementation has been made such that it can handle more advanced simulations than those investigated in this thesis. The simulations show acceptable but not ideal accuracy in both dynamic simulations and in measurements of equilibrium configurations. In addition, the reduced simulations using hyperreduction show good speedup for the more complex models investigated