A generalized power-law detection algorithm for humpback whale vocalizations

Conventional detection of humpback vocalizations is often based on frequency summation of band-limited spectrograms under the assumption that energy (square of the Fourier amplitude) is the appropriate metric. Power-law detectors allow for a higher power of the Fourier amplitude, appropriate when th...

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Bibliographic Details
Published in:The Journal of the Acoustical Society of America 2012-04, Vol.131 (4), p.2682-2699
Main Authors: Helble, Tyler A., Ierley, Glenn R., D'Spain, Gerald L., Roch, Marie A., Hildebrand, John A.
Format: Article
Language:English
Subjects:
Acoustics
Acoustics - instrumentation
Algorithms
Animals
Biological and medical sciences
Ear and associated structures. Auditory pathways and centers. Hearing. Vocal organ. Phonation. Sound production. Echolocation
Equipment Design
Exact sciences and technology
Fourier Analysis
Fundamental and applied biological sciences. Psychology
Fundamental areas of phenomenology (including applications)
Humpback Whale - physiology
Noise, Transportation
Physics
Ships
Signal-To-Noise Ratio
Sound Spectrography
Underwater sound
Vertebrates: nervous system and sense organs
Vocalization, Animal - physiology
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Summary:Conventional detection of humpback vocalizations is often based on frequency summation of band-limited spectrograms under the assumption that energy (square of the Fourier amplitude) is the appropriate metric. Power-law detectors allow for a higher power of the Fourier amplitude, appropriate when the signal occupies a limited but unknown subset of these frequencies. Shipping noise is non-stationary and colored and problematic for many marine mammal detection algorithms. Modifications to the standard power-law form are introduced to minimize the effects of this noise. These same modifications also allow for a fixed detection threshold, applicable to broadly varying ocean acoustic environments. The detection algorithm is general enough to detect all types of humpback vocalizations. Tests presented in this paper show this algorithm matches human detection performance with an acceptably small probability of false alarms ( P FA
ISSN:0001-4966
1520-8524
DOI:10.1121/1.3685790