Correlations and Copulas for Decision and Risk Analysis
Management Science
Novelty detection: a review—part 1: statistical approaches
Signal Processing
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
An extended energy detector for non-Gaussian and non-independent noise
Signal Processing
Improved energy detector for random signals in Gaussian noise
IEEE Transactions on Wireless Communications
A Parametric Copula-Based Framework for Hypothesis Testing Using Heterogeneous Data
IEEE Transactions on Signal Processing
Generalized Matched Subspace Filter for Nonindependent Noise Based on ICA
IEEE Transactions on Signal Processing
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One-class detector is an option to deal with the problem of detecting an unknown signal in a background noise, as it is only necessary to know the noise distribution. Thus a Gaussian copula is proposed to capture the dependence among the noise samples, meanwhile the marginals can be estimated using well-known methods. We show that classical energy detectors are particular cases of the proposed one-class detector, when Gaussian noise distribution is assumed, but are inappropriate in other cases. Experiments combining simulated noise and real acoustic events have confirmed the superiority of the proposed detectors when noise is non-Gaussian. An interpretation of the methods in terms of the Edgeworth expansion is also included.