This paper presents a novel methodology in which the Unified Parkinson's Disease Rating Scale (UPDRS) data processed with a rule-based decision algorithm is used to predict the state of the Parkinson's Disease patients. The research was carried out to investigate whether the advancement of the Parkinson's Disease can be automatically assessed. For this purpose, past and current UPDRS data from 47 subjects were examined. The results show that, among other classifiers, the rough set-based decision algorithm turned out to be most suitable for such automatic assessment.Virtual slidesThe virtual slide(s) for this article can be found here:http://www.diagnosticpathology.diagnomx.eu/vs/1563339375633634.
The objective of the present work is to automatically extract information from monophonic sounds. This process consists of several stages, namely, preprocessing, parameterization, and classification. This paper shows a thorough study on the wavelet-based parameterization of musical instrument sounds and automatic recognition by means of artificial neural networks (ANNs). First, an engineered method of pitch detection is presented and exemplified by several analyses. A short discussion on error associated with automatic pitch tracking is also included. Then, examples of time-frequency analyses of various musical instrument groups are presented. The analyses are performed employing a database containing musical sounds recorded at the Sound and Vision Engineering Department, Technical University of Gdansk. On the basis of such analyses a set of parameters is derived. Feature vector properties are then discussed. For that purpose Fisher statistics is used. It allows checking the separability between musical instrument pairs. In addition, for the purpose of automatic recognition of musical instrument groups artificial neural networks are used. Various structures and training methods of the ANNs are examined. Exemplary results obtained in the carried out investigations are provided and analyzed. Concluding remarks concerning further development of such experiments are also included in the paper.
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