Deep Learning Applied to Animal Linguistics
Even nowadays, people have only a very limited understanding about animal communication. Scientists are still far from identifying statistically relevant, animal-specific, and recurring linguistic paradigms. However, combined with the associated situation-specific behavioral observations, these patt...
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| Format: | Doctoral or Postdoctoral Thesis |
| Language: | English |
| Published: |
2023
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| Online Access: | https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/23926 https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-239268 https://opus4.kobv.de/opus4-fau/files/23926/Dissertation-Bergler-Christian.pdf |
| Summary: | Even nowadays, people have only a very limited understanding about animal communication. Scientists are still far from identifying statistically relevant, animal-specific, and recurring linguistic paradigms. However, combined with the associated situation-specific behavioral observations, these patterns represent an indispensable basis for decoding animal communication. In order to derive statistically significant communicative and behavioral hypotheses, sufficiently large audiovisual data volumes are essential covering the animal-specific communicative and behavioral repertoire in a representative, natural, and realistic manner. Hence, passive audiovisual monitoring techniques are increasingly deployed to obtain more natural insights, since the recording is performed in an unobtrusive fashion to minimize disruptive factors and simultaneously maximizing the probability of observing the entire inventory of natural communicative and behavioral paradigms in adequate numbers. Nevertheless, time- and human-resource constraints hamper scientists to efficiently process large-scale noise-heavy data archives, incorporating massive amounts of hidden audiovisual information, to derive an overall and bigger picture about animal linguistics. Thus, in order to perform a deep and detailed data analysis to derive real-world representations, the support of machine-based data-driven algorithms is a fundamental prerequisite. In the scope of this doctoral thesis, a hybrid approach between machine (deep) learning and animal bioacoustics is presented, applying a wide variety of different and novel algorithms to analyze large-scale, noise-heavy, audiovisual, and animal-specific data repositories in order to provide completely new insights into the field of animal linguistics. Due to their complex social, communicative, and cognitive abilities, the largest member of the dolphin family – the killer whale (Orcinus orca) – was chosen as target species and prototype for this study. In northern British Columbia one of the largest ... |
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