Doctoral Degrees (Electrical and Electronic Engineering)

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Browsing Doctoral Degrees (Electrical and Electronic Engineering) by Subject "Acoustic emission testing"

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    How to automatically detect calls from cetaceans and fish without large-scale training data
    (Stellenbosch : Stellenbosch University, 2024-03) Van Wyk, Jacques; Versfeld, Jaco; Du Preez, Johan; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: Passive acoustic monitoring has become pivotal in the acquisition of vocalisations produced by marine wildlife, however, there is a need for suitable detection methods that can segment these vocalisations from the many hours of noise present in the retrieved audio recordings. Automatic detection methods can aid in this, labelling potential animal calls in bioacoustic recordings faster than manual annotation. One problem modern automatic detection methods suffer from, particularly deep learning methods, is overfitting due to small training sets. This calls for detection methods that can quickly and accurately detect bioacoustic vocalisations without the need for copious amounts of training data. Such detection methods are proposed here for the automatic detection of cetacean and fish vocalisations in underwater recordings. The first method is a silence detector based on a temporal power calculation that can be used as a pre-processing step to quickly find potential target calls. The harmonic structure of vocalisations is then exploited to aid in their detection. The second method is based on the summation of sound harmonics, with a normalisation scheme that provides robust performance in the presence of wide-band noise. The third method extends this normalisation scheme to spectrogram masking, a faster alternative to spectrogram cross-correlation. After this the use of unsupervised deep learning methods are investigated, in an attempt to determine their ability to detect cetacean vocalisations in the presence of imbalanced data.