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Displaying bioacoustic directional information from sonobuoys using “azigrams”
Author(s) -
Aaron Thode,
Taiki Sakai,
Jeffrey Michalec,
Shan Rankin,
Melissa S. Soldevilla,
Bruce Martin,
Katherine H. Kim
Publication year - 2019
Publication title -
the journal of the acoustical society of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.619
H-Index - 187
eISSN - 1520-8524
pISSN - 0001-4966
DOI - 10.1121/1.5114810
Subject(s) - azimuth , acoustics , spectrogram , beamforming , computer science , bioacoustics , noise (video) , signal (programming language) , signal processing , signal to noise ratio (imaging) , sonar , speech recognition , artificial intelligence , telecommunications , physics , optics , radar , image (mathematics) , programming language
The AN/SSQ-53 Directional Frequency Analysis and Recording (DIFAR) sonobuoy is an expendable device that can derive acoustic particle velocity along two orthogonal horizontal axes, along with acoustic pressure. This information enables computation of azimuths of low-frequency acoustic sources from a single compact sensor. The standard approach for estimating azimuth from these sensors is by conventional beamforming (i.e., adding weighted time series), but the resulting "cardioid" beampattern is imprecise, computationally expensive, and vulnerable to directional noise contamination for weak signals. Demonstrated here is an alternative multiplicative processing scheme that computes the "active intensity" of an acoustic signal to obtain the dominant directionality of a noise field as a function of time and frequency. This information is conveniently displayed as an "azigram," which is analogous to a spectrogram, but uses color to indicate azimuth instead of intensity. Data from several locations demonstrate this approach, which can be computed without demultiplexing the raw signal. Azigrams have been used to help diagnose sonobuoy issues, improve detectability, and estimate bearings of low signal-to-noise ratio signals. Azigrams may also enhance the detection and potential classification of signals embedded in directional noise fields.

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