Caracterización hidroacústica y clasificación automática de agregaciones de organismos marinos.
Hydroacoustic techniques are currently the most efficient tool to study the aquatic environments remotely, giving a continuous high spatial resolution sampling. However, this technique has a low specific resolution thus restriction on the echo recordings identification. Therefore, the scrutinizing process is very difficult in multispecific areas. The standard procedure for the biological identification is via direct sampling methods, such as sub-aquatic video recordings or net trawling. However, the information obtained is discrete, restricting identify only a small segment of the acoustic information acquired continuously. At the moment, in order to reduce the irresolution, different techniques are available for post-processing and analysis the acoustic signals. In this thesis, are presented different methods for acoustic signal characterization and automatic classification of marine organism's aggregations. Are included topics on the application of theoretical predictive models based on the capacity of the organisms for backscatter the sound, fish school variable extraction and multi-frequency analysis. Automatic classification of organism's aggregations using artificial neural networks was intensively explored for both, the successful identification of different fish species schools and classification of anchovy (Engraulis anchoita) sizes by means acoustical descriptors extracted from the schools (Argentine Sea). The discussed subjects take advantage in the scientific field generating knowledge on marine ecosystems; and in the socio-economic field, serving the sustainable management of fisheries resources of high significant commercial importance for Argentina.
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| Format: | Thesis/Dissertation biblioteca |
| Language: | Spanish / Castilian |
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Universidad Nacional del Comahue. Centro Regional Universitario Bariloche
2017
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| Subjects: | Bioacústica, Ecosondeo, Modelos acústicos, Procesamiento de imágenes, Imágenes acústicas, Inteligencia artificial, Peces marinos, Comportamiento de cardumen, Tamaño, Frente de estuarios, ASFA_2015::B::Bioacoustics, ASFA_2015::E::Echosounding, ASFA_2015::A::Acoustic models, ASFA_2015::I::Image processing, ASFA_2015::A::Acoustic imagery, ASFA_2015::A::Artificial intelligence, ASFA_2015::M::Marine fish, ASFA_2015::S::Schooling behaviour, ASFA_2015::S::Size, ASFA_2015::E::Estuarine front, |
| Online Access: | http://hdl.handle.net/1834/10995 |
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