The aim of this paper is the detection of a bioacoustic signal embedded in several noises such as sea noise and other bioacoustic signals (dolphins, sperm whales). All the signals are real world signals.Only second order statistics are use through the estimated correlation matrices of the signals.
This paper proposes an extension of the Constrained Stochastic Matched Filter (CSMF) based on the optimization of the Signal to Noise Ratio after linear filtering. The approach proposed is a multicriteria one, merging three different versions of the CSMF, and is named Multicriteria CSMF (MCSMF).The objective is that the results obtained are better than the other methods, or at least equal to the best among the three.The results are provided on ROC curves and the method is compared to the classical method Stochastic Matched Filter (SMF).
This paper introduces a new fast algorithm named CSMFST which estimates the p-dimensional optimal subspace, i.e. where the signal-to-noise ratio is maximized in the case of n-dimensional nonstationary signals. We assume that we treat both signal and noise which are characterized by their samples. This algorithm is an SP-type algorithm and uses the same principles as the Yet Another Subspace Tracking (YAST) algorithm when estimating the covariance matrices. At each step, it estimates a matrix which spans the optimal subspace.
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