Automatic event detection for long-term monitoring of hydrophone data

In this paper, we propose an efficient method for long-term monitoring of a wide variety of marine mammals and human related activities using hydrophone data. The proposed method uses a combination of a two-stage denoising process followed by a new event detection function that estimates temporal predictability. The detection function utilizes long-term and short-term predictions in order to detect various acoustic events from the background noise. The first stage of the denoising process uses temporal decomposition via Empirical Mode Decomposition to improve the correct detection rate, while the second stage uses Wavelet Packet spectral decomposition to reduce the false detection rate. Applied to event detection in NEPTUNE hydrophone recordings, the method demonstrates an accuracy of 95% and an F-measure of 94%.