Jorge Moragues

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The problem of acoustic detection and recognition is of particular interest in surveillance applications, especially in noisy environments with sound sources of different nature. Therefore, we present a multiple energy detector (MED) structure which is used to extract a new set of features for classification, called frequency MED (FMED) and combined MED(More)
Energy detectors are optimum to detect uncorrelated Gaussian signals or GLRT to detect completely unknown signals; in both cases, noise background must be uncorrelated Gaussian. However, energy detectors degrade when noise background is non-independent and nonGaussian. An extension is presented in this paper to deal with this situation. Independence is(More)
In this paper, we present a novel approach for detection and localization of both impulsive and non-impulsive sound sources. At first, theoretical basics of the used algorithms are presented. Subsequently, we describe a standard SRP-PHAT based localization method and discuss occurring complications, especially for impulsive sound sources. Therefore, a(More)
Standard energy detectors (ED) are optimum to detect unknown signals in presence of uncorrelated Gaussian noise. However, in real applications the signal duration and bandwidth are unpredictable and this fact can considerably degrade the detection performance if the appropriate observation vector length is not correctly selected. Therefore, a multiple(More)
A microphone array can be employed to localize dominant acoustic sources in a given noisy environment. This capability is successfully used in good signal to noise ratio (SNR) conditions but its accuracy decreases considerably in the presence of other background noise sources. In order to counteract this effect, a novel approach that combines the(More)
We propose a generalization of the matched subspace filter for the detection of unknown signals in a background of non-Gaussian and nonindependent noise. The generalization is based on a modification of the Rao test by including a linear transformation derived from independent component analysis (ICA). Receiver operating characteristic (ROC) curves computed(More)