Seecology: Data Visualization Framework for Soundscape Ecology Applications

• Ano de defesa: 2020
• Autor(a) principal: Reis, Clausius Duque Gonçalves
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Acoustic ecology; Análise de áudio; Audio analysis; Boat detection; Detecção de barcos; Ecologia acústica; Extração de características; Feature extraction; Framework; Radviz; Soundscape; Visualização; Visualization
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-10092020-155103/

The field of Soundscape Ecology refers to the study of sounds produced in natural environments and how they can provide important information about the state of the environment, as well as on the potential impacts caused by changes due to external influences. The analysis and visualization of large amounts of ecological recordings, as well as the development of appropriate tools for audio analysis contitute a major challenge. Mechanisms for extracting audio features, as well as the characterization of acoustic events of interest, resulting in datasets that capture the frequency variations and the occurrence of acoustic events in the recordings, still constitute a problem due to available solutions do not prove adequate for data analysis in acoustic ecology research, involving domain-specific issues and voluminous amounts of audio records collected over long periods of time. This work aims to address problems related to the extraction of audio features, providing assistance through visualization to the selection of the most significants, that could represent the subtle variations in ecological recordings, as well as assisting specialists in the generation of annotated dtasets by the characterization of acoustic events through exploratory visualizations, and methods for detecting vessels in underwater recordings. A framework named Seecology is presented, encompassing suitable methods and tools to supporting specialists and scholars of environmental analysis. Case studies were carried out with the framework in terrestrial and underwater recordings provided by acoustic ecology researchers, by producing datasets from the custom feature extractor included in the framework, and in the case of the method developed for detecting boats in underwater recordings, a comparative study to another method was conducted to determine its accuracy, in addition to the case study to determine its effectiveness. The presented methods for extracting characteristics, characterizing acoustic events through exploratory visualization and boat detection, demonstrated their effectiveness for applications in acoustic ecology, with the framework containing the methods capable of producing multidimensional datasets without excessive computational costs, allowing the user to easily generate annotations on this data through the included visualizations. The boat detection method performed better than the one it was compared, both in speed and accuracy, being able to detect weak signals from boats even under extreme noise.