Temporal separation of whale vocalizations from background oceanic noise using a power calculation
This work addresses the arduous task of cetacean vocalization analysis for marine biologists and researchers, offering an incremental improvement in efficiency and robustness over existing methods.
The paper tackles the challenge of detecting whale vocalizations in noisy underwater audio by developing a computationally efficient detector based on a robust power calculation and recursive statistics. The detector shows good performance at moderate-to-high signal-to-noise ratios, outperforming an energy detector and a deep learning method in tests with southern right whale sounds.
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.