A new event detection method for noisy hydrophone data

In this paper, a new method for detecting events in noisy hydrophone data is developed. The method takes an image processing approach to the 1D hydrophone data by first converting it into a logfrequency spectrogram image (cepstrum). This image is then filtered by reconstructing it based on mutual information (MI) criteria of the dominant orientation map. The features of the reconstructed cepstrum are then enhanced using a combination of edge-tracking and noise smoothing. Binary feature classification on the processed cepstrum is performed using a least-squares support vector machine (LS-SVM). Compared to other methods, the proposed image-based event detection method exploits both the scale and the orientation information. The method showed that the detection performance in terms of binary classification sensitivity with more than 55% for rare events such as whale calls from noisy hydrophone recordings (a database size of over 160 h (1943 x 5 min) with a total of 39 events) from the NEPTUNE Canada project, with more than 99% specificity and 98% overall accuracy. With relatively low computational cost and high accuracy, the presented method is useful for automated long-term monitoring of a wide variety of marine mammals and human related activities from hydrophone data. The effectiveness of the proposed method is demonstrated over a large number of real noisy hydrophone recordings. (C) 2019 Elsevier Ltd. All rights reserved.