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Advisor(s)
Abstract(s)
Este trabalho de dissertação de mestrado tem o intuito de aplicar técnicas de clustering em um conjunto de dados a fim de verificar a possibilidade de agrupamento entre grupos que contém informações a respeito da voz. Neste estudo são empregados os parâmetros acústicos jitter absoluto, jitter relativo, shimmer relativo, autocorreleção, Harmonic to Noise Ratio e Noise to Harmonic Ratio. Estes atributos são extraídos da base de dados Saarbrücken Voice Database, a partir de ficheiros de áudio que dispõem das vogais /a/, /i/e/u/ sustentadas nos tons alto, baixo e normal. Para o agrupamento dos conjuntos através desses parâmetros são utilizados o grupo de Controlo, o Carcinoma, o Cisto, a Disfonia Espasmódica, a Disfunção Funcional, a Disfunção Psicogênica, o Edema de Reinke, o Granuloma, o Granuloma por Intubação. a Laringite Crônica, a Paralisia das Cordas Vocais, Pólipos das Cordas Vocais, o Tumorna Hipofaringe e o Tumor na Laringe. Esta invetigação ocorre ao empregar 2 caminhos diferentes para averuiguar a possibilidade de agrupar patalogias: a análise estatística descritiva e algoritmos de agrupamento. Para a análise estatística descritiva, utiliza-se o Diagrama de Caixas. Para os métodos de agrupamento, são empregados os Mapas Auto- Organizáveis de Kohonen e o clustering hierárquico aglomerativo. Os resultados dessas categorias apontam a divisão dos dados em 2 conjuntos, em que um grupo contém grande parte das amostras, enquanto o outro contém apenas algumas amostras. No entanto, essas técnicas se divergem ao comparar os elementos pertencentes aos conjuntos, pois, na análise estatística descritiva as amostras se encontram maia bem separadasd do que nos métodos de clustering.
This dissertation work aims to apply clustering techniques to a set of data in order to verify the possibility of clustering between groups that contain information about the voice. In this study, the acoustic parameters absolute jitter, relative jitter, absolute shimmer, relative shimmer, autocorrelation, Harmonic to Noise Ratio and Noise to Harmonic Ratio are used. These attributes are extracted from the Saarbrücken Voice Database, from audio files that have the vowels /a/, /i/ and /u/ sustained in a high, low and normal tones. For the clustering of the sets using these parameters, the Control group, Carcinoma, Cyst, Spasmodic Dysphonia, Functional Dysphonia, Hyperfunctional Dysphonia, Hypofunctional Dysphonia, Hypotonic Dysphonia, Psychogenic Dysphonia, Reinket s Edema, Granuloma, Intubation Granuloma, Chronic Laryngitis, Vocal Cord Paralysis, Vocal Cord Polyps, Hypopharyngeal Tumor and Laryngeal Tumor are utilized. This investigation occurs when employing 2 different ways to investigate the possibility of grouping pathologies: the descriptive statistical analysis and clustering algorithms. For the descriptive statistical analysis, the Box Diagram is used. SelfOrganizing Maps of Kohonen and agglomerative hierarchical clustering are utilized for grouping methods. The results of these 2 categories point to the division of the data into 2 sets, in which one group contains a large part of the samples, while the other contains only a few samples. However, these techniques differ when comparing the elements belonging to the sets, because, in the descriptive statistical analysis, the samples are better divided than in the clustering methods.
This dissertation work aims to apply clustering techniques to a set of data in order to verify the possibility of clustering between groups that contain information about the voice. In this study, the acoustic parameters absolute jitter, relative jitter, absolute shimmer, relative shimmer, autocorrelation, Harmonic to Noise Ratio and Noise to Harmonic Ratio are used. These attributes are extracted from the Saarbrücken Voice Database, from audio files that have the vowels /a/, /i/ and /u/ sustained in a high, low and normal tones. For the clustering of the sets using these parameters, the Control group, Carcinoma, Cyst, Spasmodic Dysphonia, Functional Dysphonia, Hyperfunctional Dysphonia, Hypofunctional Dysphonia, Hypotonic Dysphonia, Psychogenic Dysphonia, Reinket s Edema, Granuloma, Intubation Granuloma, Chronic Laryngitis, Vocal Cord Paralysis, Vocal Cord Polyps, Hypopharyngeal Tumor and Laryngeal Tumor are utilized. This investigation occurs when employing 2 different ways to investigate the possibility of grouping pathologies: the descriptive statistical analysis and clustering algorithms. For the descriptive statistical analysis, the Box Diagram is used. SelfOrganizing Maps of Kohonen and agglomerative hierarchical clustering are utilized for grouping methods. The results of these 2 categories point to the division of the data into 2 sets, in which one group contains a large part of the samples, while the other contains only a few samples. However, these techniques differ when comparing the elements belonging to the sets, because, in the descriptive statistical analysis, the samples are better divided than in the clustering methods.
Description
Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do Paraná
Keywords
Análise estatística descritiva Mapas auto-organizacionais de Kohonen Clustering hierárquico Técnicas de clustering Parâmetros acústicos Diagrama de caixa