Classification of Parkinson’s Disease using MultiPass Lvq,Logistic Model Tree,K-Star for Audio Data set : Classification of Parkinson Disease using Audio Dataset

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.

The Björling 'Opera' : A children's Nursery Academy and an Italian Conservatory in Miniature

The aim in this chapter is to develop a deeper understanding about the informal Björling 'School' in Sweden. Contextually the example is related to the micro history of opera education contributing to the macro perspective illuminating a provincial example of the concept of domestic opera schooling. The specific focus was on Karl David Björling (1873-1926), the teaching parent of the Swedish tenor Jussi Björling (1911-1960) and his brothers Gösta and Olle.   The Björling family model of opera schooling belongs to the classical canon of domestic home education which was common during the epoch. The phenomenon is also within the field of opera singing an important reference to the historical context of the Nordic opera history of vocal education.   The uniqueness concerning the Björling School seems to be the rigorous and exceptionally early training. David Björling’s pedagogy was rooted in earlier German theories of musical upbringing. It's clear from his results that he was familiar with the neo-humanistic ideal on which reformed music education was based. Of a specific interest is the term Gesang als Unterricht as a concept for developing childrens musical and memorising capacities.   Conceptually the roots of the Björling model are in the eighteenth-century Romantic view of prodigies and their abilities. The extensive touring is connected to the promotion of wonder-children, and David Björling’s educational style to the conservative Master-pupil tradition.   David Björling's vocal ideal was a part of the contemporary debate about “The decadence of the singing art”, and seems to have its roots in an older Italian tradition. There are recurring similarities between his educational methods and the didactic principles of the Lamperti School: Enjoying a revival around the late 1800s and early 1900s, it has been called the natural or the national school. Nevertheless, through authentic experiences and gramophone recordings the Italian tenor Enrico Caruso became David Björling’s pedagogical role model.