Temporal separation of whale vocalizations from background oceanic noise using a power calculation

TL;DR

This paper introduces a computationally efficient and noise-resistant method for detecting whale vocalizations in underwater audio, improving the process of analyzing cetacean sounds amidst background noise.

Contribution

A new detection method based on power calculation and recursive mean-variance estimation that is simple, fast, and robust against noise, outperforming some existing techniques.

Findings

Performs well at moderate-to-high SNR levels

Easy to implement and computationally efficient

Robust detection of whale calls in noisy environments

Abstract

The process of analyzing audio signals in search of cetacean vocalizations is in many cases a very arduous task, requiring many complex computations, a plethora of digital processing techniques and the scrutinization of an audio signal with a fine comb to determine where the vocalizations are located. To ease this process, a computationally efficient and noise-resistant method for determining whether an audio segment contains a potential cetacean call is developed here with the help of a robust power calculation for stationary Gaussian noise signals and a recursive method for determining the mean and variance of a given sample frame. The resulting detector is tested on audio recordings containing southern right whale sounds and its performance is compared to a contemporary energy detector and a popular deep learning method. The detector exhibits good performance at moderate-to-high signal-to-noise ratio values. The detector succeeds in being easy to implement, computationally efficient to use and robust enough to accurately detect whale vocalizations in a noisy underwater environment.