Principal components-based hidden Markov model for automatic detection of whale vocalisations

Over the years, researchers have continued to put forward solutions to lessen the threats faced by whales within their ecosystem. The correct detection of the different species of whale is important in the search for solutions that will lessen the threats. In order to accurately detect and classify whale species, a number of techniques have been proposed over the years, with varying degrees of success. This research seeks to improve the performance of the hidden Markov models (HMMs), which is one of the most consistent methods for the detection and classification of whale vocalisations. The performance of HMMs is affected by the quality of the feature vectors fed into them. Thus, this research proposes feature extraction (FE) techniques based on principal component analysis. The principal components (PC) computed from PCA and kernel PCA were uniquely transformed into feature vector structures suitable for the HMMs. The emerging models, PCA-HMMs and kPCA-HMMs, were experimented with on passive acoustic monitoring (PAM) datasets containing southern right whale and humpback whale vocalisations. The results from the experiments showed that the kPCA-HMMs outperformed PCA-HMMs. This is due to kPCA's ability to find non-linear subspaces that may exist in whale vocalisations. Furthermore, the results of the developed PC-HMMs were compared with other existing FE techniques used with HMMs in the literature for the detection of whale vocalisations. The proposed PC-HMMs did not only outperform the existing FE-HMMs but also offered lower computational complexity than the existing HMMs for the detection of whale vocalisations.