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A source separation approach to enhancing marine mammal vocalizations
M Berke Gur1, Christopher Niezrecki
1Department of Mechatronics Engineering, Bahcesehir University, Besiktas, Istanbul 34353, Turkey. berke.gur@bahcesehir.edu.tr
The Journal of the Acoustical Society of America
|December 17, 2009
Summary
This study introduces a new blind source separation algorithm to enhance marine mammal vocalizations contaminated by noise. The method improves detection range fivefold using just two hydrophones, aiding passive acoustic monitoring.
Area of Science:
- Marine bioacoustics
- Signal processing
- Underwater acoustics
Background:
- Passive acoustic monitoring (PAM) of marine mammals is crucial but often hampered by noise interference, such as from vessels.
- Conventional noise suppression techniques like beamforming require complex arrays and may struggle with low signal-to-noise ratios (SNR).
- Developing effective underwater signal enhancement methods is essential for accurate marine mammal population assessments.
Purpose of the Study:
- To propose and implement an alternative multi-channel underwater signal enhancement technique.
- To develop a blind source separation (BSS) algorithm for extracting marine mammal vocalizations from noisy two-channel measurements.
- To assess the algorithm's effectiveness in improving the detection range of passive acoustic detectors.
Main Methods:
- A novel blind source separation algorithm utilizing a robust decorrelation criterion was derived and implemented.
- The algorithm was adapted to an unsupervised framework by incorporating the supervised affine projection filter update rule to handle temporally correlated data.
- The method was evaluated using real West Indian manatee (Trichechus manatus latirostris) vocalizations and recorded watercraft noise.
Main Results:
- The proposed BSS algorithm successfully separated manatee vocalizations from background noise, even in low SNR conditions.
- The algorithm demonstrated suitability for low SNR measurements, a common challenge in marine acoustic monitoring.
- The method improved the detection range of a passive acoustic detector by an average factor of five for input SNRs between -10 and 5 dB.
Conclusions:
- The developed blind source separation algorithm offers a robust and effective alternative for underwater acoustic signal enhancement.
- The algorithm's ability to improve detection range significantly enhances the utility of passive acoustic monitoring systems, especially with limited hydrophone arrays.
- This approach is particularly valuable for monitoring species like the West Indian manatee in noisy environments, potentially increasing conservation efforts.
