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Identifier 000434012
Title Machine learning techniques for the detection of illegal human activity in audio recordings from protected areas
Alternative Title Χρήση τεχνικών μηχανικής μάθησης για τον εντοπισμό ανθρωπογενούς δραστηριότητας από ηχογραφήσεις σε προστατευόμενες περιοχές
Author Ψαρουλάκης, Κωνσταντίνος Σ.
Thesis advisor Τσακαλίδης, Παναγιώτης
Reviewer Στεφανάκης, Νικόλαος
Στυλιανού, Γιάννης
Abstract Human activity is considered today as the primary reason for habitat loss for a large number of Earth's plant and animal species. This activity results to the permanent loss of species and to the weakening of the ecosystems that are of significant importance for the overall health of the planet and as a consequence, to the quality of the human life. One key measure to protect habitats is the establishment of protected areas where human activity is restricted. In these areas, systems employing multiple cameras and microphones may offer a significant assistance in monitoring the health of the ecosystem but also as the means to prevent human intervention that is harmful to the environment. This Thesis concerns the application of signal processing and machine learning techniques to audio recordings acquired in protected areas in Greece, with the aim to automatically detect sound events that are indicative of illegal human activity such as illegal logging, grazing, hunting, etc. To collect and annotate the data that is required for training such a scheme, we illustrate the usefulness of a Voice Activity Detector (VAD) that is activated on the presence of harmonic structure in the audio content. The VAD is used in order to automatically segment hundreds of hours of audio recording into thousands of short duration audio clips that potentially carry the underlying pattern of interest. Continuing, we perform numerous experiments with the goal to find the optimal approach for (i) a binary classification problem that focuses on the case of chainsaw sound and (ii) a six class problem that includes additional patterns relating to illegal human activity. Experimental results illustrate the superiority of Deep Neural Networks (DNN) against other well-known conventional classifiers and furthermore, highlight choices that are advantageous for the intended task in terms of the DNN architecture and the type of acoustic features.
Language English
Subject Chainsaw
Neural network
Μηχανική μάθηση
Νευρωνικά δίκτυα
Παράνομη ανθρωπογενής δραστηριότητα
Issue date 2020-11-27
Collection   Faculty/Department--Faculty of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
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