Fin whale pulse detection with deep neural networks

Abstract

In this paper, we present an application of Deep Neural Networks to the detection of Fin Whales (Balaenoptera physalus) pulses, also known as calls, from very long acoustic recordings. For the purpose of detection, acoustic signals are converted to images using a Fourier transform operation. Therefore, acoustic pulses become specific shapes. The detection of pulses and their seasonal distribution is used by biologists to estimate the presence of animals and understand their behavior, and they have important ecological value. However, the variations in shape and the presence of background noise make detection difficult. The use of automated instruments for this task is crucial to processing the huge amount of data that comes from hundreds of hours of recordings in a fast and effective way. In this work, different network architectures are compared to assess their effectiveness in the task on a subset of recordings. Additionally, these are compared with other Machine Learning methods from the literature. The best performing network architecture is then applied to the whole set, consisting of three months of recordings, showing an accuracy of 81.37% and a precision of 75%, comparable to the results obtained with other methods.