Software Corrections of Vocal Disorders

We discuss how vocal disorders can be post-corrected via a simple nonlinear noise reduction scheme. This work is motivated by the need of a better understanding of voice dysfunctions. This would entail a twofold advantage for affected patients: Physicians can perform better surgical interventions and on the other hand researchers can try to build up devices that can help to improve voice quality, i.e. in a phone conversation, avoiding any surgigal treatment. As a first step, a proper signal classification is performed, through the idea of geometric signal separation in a feature space. Then through the analysis of the different regions populated by the samples coming from healthy people and from patients affected by T1A glottis cancer, one is able to understand which kind of interventions are necessary in order to correct the illness, i.e. to move the corresponding feature vector from the sick region to the healthy one. We discuss such a filter and show its performance.Comment: Computer Methods and Programs in Biomedicine, accepted for publicatio

BioVoice: a multipurpose tool for voice analysis

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. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Analysis of Vocal Disorders in a Feature Space

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 an 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.Comment: 12 pages, 3 figures, accepted for publication in Medical Engineering & Physic

Методы и алгоритмы обработки мелкомасштабной структуры метеорологических полей в приземной атмосфере

В данной работе объектом исследования являются метеорологические поля наблюдений, полученные с ультразвуковых термоанемометров. Работа нацелена на разработку методов и алгоритмов обработки мелкомасштабной структуры метеорологических полей в приземной атмосфере. В процессе исследования были изучены возможности аппаратно-программного функционала метеорологического комплекса АМК-03 для получения высокочастотных измерений метеорологических параметров. Исследованы и разработаны параллельные алгоритмы при обработке наблюдений ультразвукового термоанемометра. Разработана структурно-функциональная схема программного комплекса обработки метеорологических данных ультразвуковых метеостанций, позволяющего на заданной выборке метеорологических данных провести первичный статистический анализ.This work contains meteorological observation fields obtained from ultrasonic thermal anemometers. The work is aimed at developing methods and algorithms for processing the fine-scale structure of meteorological fields in the surface atmosphere. During the research, the capabilities of the hardware-software functional of the AMK-03 meteorological complex were studied to obtain high-frequency measurements of meteorological parameters. Parallel algorithms were studied and developed in the processing of observations of an thermoanemometer. A structural-functional scheme of a software complex for processing meteorological data of ultrasonic weather stations has been developed, which allows performing a primary statistical analysis on a given sample of meteorological data