Temporal separation of whale vocalizations from background oceanic noise using a power calculation |
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| Affiliation: | 1. School of Mathematics and Physics, Anqing Normal University, 246133 Anqing, China;2. Research Center of Aquatic Irganism Conservation and Water Ecosystem Restoration in Anhui Province, Anqing Normal University, 246133 Anqing, China;3. College of Life and Science, Anqing Normal University, 246133 Anqing, China;4. School of Mathematics and Computer, Tongling University. 244061 Tongling, China;5. University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, 246133 Anqing, China;1. School of Biological Sciences, The University of Queensland, Brisbane 4072, Australia;2. Australian Rivers Institute, Griffith University, Nathan 4111, Australia;3. CSIRO Land & Water, Dutton Park, Brisbane 4102, Australia;4. Department of Environment, Land, Water and Planning, Victorian Government, Casterton, 3311, Australia;5. School of Earth and Environmental Sciences, The University of Queensland, Brisbane 4072, Australia;6. Department of Zoology, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa;7. Department of Zoology and Entomology, University of Pretoria, Pretoria 0002, South Africa |
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| 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. |
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| Keywords: | Gaussian noise Detection Whale vocalizations Power Variance Segmentation |
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