Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Multidimensional Systems and Signal Processing
Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
Online Clustering Algorithms for Radar Emitter Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the Support of a High-Dimensional Distribution
Neural Computation
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
Adaptive threshold determination for spectral peak classification
Computer Music Journal
Noise enhanced nonparametric detection
IEEE Transactions on Information Theory
An active contour algorithm for spectrogram track detection
Pattern Recognition Letters
Spectrogram segmentation by means of statistical features for non-stationary signal interpretation
IEEE Transactions on Signal Processing
A detailed investigation into low-level feature detection in spectrogram images
Pattern Recognition
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Spectrogram analysis of acoustical sounds for underwater target classification is utilized when loud nonstationary interference sources overlap with a signal of interest in time but can be separated in time-frequency (TF) domain. We propose a signal masking method which in a TF plane combines local statistical and morphological features of the signal of interest. A dissimilarity measure D of adjacent TF cells is used for local estimation of entropy H&&, followed by estimation of ΔH&& = H&&tc&&- H&&fc&&entropy difference, where H&&fc&&is calculated along the time axis at a mean frequency fc&& and H&&tc&&is calculated along the frequency axis at a mean time tc&& of the TF window, respectively. Due to a limited number of points used in ΔH&& estimation, the number of possible ΔH&& values, which define a primary mask, is also limited. A secondary mask is defined using morphological operators applied to, for example, H&& and ΔH&&. We demonstrate how primary and secondary masks can be used for signal detection and discrimination, respectively. We also show that the proposed approach can be generalized within the framework of Genetic Programming.