Frequency‐Selective Detection of Signals in Noise

Detection and Estimation of Signals in Noise

Robust detection of signals in dependent noise

The robust detection of signals in additive dependent noise is considered. The solution to the finite-sample problem is obtained when the Bayes risk is used as the performance measure. For the multivariate densities involved we assume that they belong to an c-contamination model. The robust detection structure is shown to be optimum for the least-favorable density and is a censored version of t...

Detection of Coherent Bioacoustic Signals in Underwater Noise

Challenges in signal detection in the presence of noise arise in many underwater acoustic applications such as active and passive sonar, geophysical surveying, and marine life monitoring. The detection problem is generally formulated within the framework of binary hypothesis testing. An important class of detectors, known as the locally optimum detectors, are designed based on the expectation t...

Stochastic model for detection of signals in noise.

Fifty years ago Birdsall, Tanner, and colleagues made rapid progress in developing signal detection theory into a powerful psychophysical tool. One of their major insights was the utility of adding external noise to the signals of interest. These methods have been enhanced in recent years by the addition of multipass and classification-image methods for opening up the black box. There remain a ...

Automatic Detection and Removal of Impulsive Noise in Audio Signals

This article presents a method for restoring audio signals corrupted by impulsive noise such as clicks, bursts or scratches. The algorithm takes as input a degraded audio signal and automatically detects the locations of the degraded samples and replaces them with more appropriate values. Both steps (detection and interpolation) are based on the assumption that the signal can locally be modeled...