Department of Mechatronics Engineering, Bahcesehir University, Besiktas, Istanbul 34353, Turkey.
J Acoust Soc Am. 2009 Dec;126(6):3062-70. doi: 10.1121/1.3257549.
A common problem in passive acoustic based marine mammal monitoring is the contamination of vocalizations by a noise source, such as a surface vessel. The conventional approach in improving the vocalization signal to noise ratio (SNR) is to suppress the unwanted noise sources by beamforming the measurements made using an array. In this paper, an alternative approach to multi-channel underwater signal enhancement is proposed. Specifically, a blind source separation algorithm that extracts the vocalization signal from two-channel noisy measurements is derived and implemented. The proposed algorithm uses a robust decorrelation criterion to separate the vocalization from background noise, and hence is suitable for low SNR measurements. To overcome the convergence limitations resulting from temporally correlated recordings, the supervised affine projection filter update rule is adapted to the unsupervised source separation framework. The proposed method is evaluated using real West Indian manatee (Trichechus manatus latirostris) vocalizations and watercraft emitted noise measurements made within a typical manatee habitat in Florida. The results suggest that the proposed algorithm can improve the detection range of a passive acoustic detector five times on average (for input SNR between -10 and 5 dB) using only two receivers.
在基于被动声学的海洋哺乳动物监测中，一个常见的问题是声音信号被噪声源（如水面船只）污染。提高语音信号与噪声比（SNR）的常规方法是通过对使用阵列进行的测量进行波束形成来抑制不需要的噪声源。在本文中，提出了一种多通道水下信号增强的替代方法。具体来说，推导并实现了一种从双通道噪声测量中提取语音信号的盲源分离算法。所提出的算法使用鲁棒解相关准则将语音与背景噪声分离，因此适用于低 SNR 测量。为了克服由于时间相关记录而导致的收敛限制，将监督仿射投影滤波器更新规则适用于无监督源分离框架。使用在佛罗里达州典型海牛栖息地内进行的真实西印度海牛（Trichechus manatus latirostris）语音和船只发出的噪声测量来评估所提出的方法。结果表明，所提出的算法可以在仅使用两个接收器的情况下，将被动声学探测器的检测范围平均提高五倍（对于输入 SNR 在-10 到 5 dB 之间）。
