Reconhecimento Automático de Aves da Família Tinamidae Através da Vocalização
This work presents a comprehensive approach to develop a system for recognizing birds by vocalization. The approach specifically addresses the recognition of birds of the Tinamidae family proposing the analysis of data related to the frequency and song of the bird and also classifying and determinin...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | Brasil |
| Institución: | Universidade Federal de Goiás (UFG) |
| Repositorio: | Repositório Institucional da UFG |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.bc.ufg.br:tde/977 |
| Acceso en línea: | http://repositorio.bc.ufg.br/tede/handle/tde/977 |
| Access Level: | acceso abierto |
| Palabra clave: | Reconhecimento Automático Tinamidae Processamento de Sinais Tempo-Frequência Automatic Recognition Signal Processing Time-Frequency CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
| Sumario: | This work presents a comprehensive approach to develop a system for recognizing birds by vocalization. The approach specifically addresses the recognition of birds of the Tinamidae family proposing the analysis of data related to the frequency and song of the bird and also classifying and determining the species of bird. The study differs from related research primarily for performing the pre-processing stage automatically. This stage determines the following characteristics: the minimum, the maximum and the stronger frequencies. It s still made a segmentation of the bird singing in periods of sound and silence. The time of singing is also used as a characteristic peculiar to each species analyzed. For the automatic determination of the characteristics of the frequency and song of the bird, an analysis of the power spectral density was made for each time period specified in the frequency using the spectrogram of the song. The recognition and classification technique adopted was the nearest neighbor, using Euclidean distance normalized by the standard deviation. The accuracy of the technique used was 94.12%. |
|---|