Identification of fish vocalizations from ocean acoustic data

A new method for identification of fish vocalizations based on auditory analysis and support vector machine (SVM) classification is presented. In this method, high resolution features have been extracted from fish vocalization data using the amplitude modulation spectrogram (AMS) of the input signals to facilitate the identification of grunts and growls made by a highly vocal wild fish, Porichthys notatus. The comparison results made from ocean audio recordings verify the effectiveness of the proposed method in identifying various types of fish vocalizations. The relationships between signal-to-noise ratio (SNR) and ocean temperature with the accuracy of the proposed method have also been quantified. Moreover, a context-aware prediction algorithm is introduced for estimating the continuous data.