Signal discrimination via non-Gaussian modeling with application to termite detection

Detection of weak signals in a low SNR environment is generally difficult, particularly, when the underlying signal noise is not only not Gaussian distributed but essentially unknown. A good example of such a case is the detection of termite biting signals from noisy audio data recorded by a passive acoustic sensor. In this paper, we present a novel technique to discriminate weak signals in data from noise of a learned non-Gaussian distribution. The proposed method, proceeds via the framework of generalised likelihood ratio test, and consists of two fundamental steps. First, an entropy-based incremental variational Bayesian inference is adopted to learn the non-Gaussian distribution from data using a Gaussian mixture model. An information geometric mapping of the data is then carried out via the total Bregman divergence (tBD), where the ambient noise distribution is approximated by the tBD-based l₁-norm center of the neighboring data points over a specified time window. Experiment results show that the proposed method yields a significantly improvement in detection probability in low SNR and a robust detection performance compared with existing detection techniques.