A novel approach to low frequency activity detection in highly sampled hydrophone data based on B-spline approximation

In this paper, we present a novel method for detection of low frequency signals less than 100 Hz in hydrophone data sampled at 96 KHz. The low-frequency activities (e.g. particular whale calls) in the hydrophone data are detected based on B-spline approximations of the hydrophone data. The error pattern of the incoming/detected signal and template signal is derived by calculating the MSEs (mean-square errors) between their B-spline approximations and compared with that of the reference signal and template signal. Here, the incoming signal is a detected (new/non-labeled) hydrophone data, whereas the reference signal is the ensemble of labeled hydrophone data and the template is a target signal that controls the detection. In the decision module, the threshold is selected based on the skewness of the error patterns. The performance of the method is evaluated using real recorded hydrophone data showing promising results.