  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.
