Implementation of a neural network based bicepstral classifier for marine noise sources

High-Order Domain Feature Extraction Technology for Ocean Acoustic Observation Signals: A Review

“…Therefore, the results obtained when bispectrum is applied to detection and classification such as small and medium-sized vessel monitoring tend to have a higher SNR than the original data [26]- [34]. Moreover, since higher-order spectroscopy has the advantage of revealing the phase coupling mode of the signal [35], [36], bispectrum can be used to determine the phase coupling between the signals that generate certain spectral peaks, thus enabling the spectral peaks to be associated with different physical generation mechanisms.…”

“…Where P represents signal energy. As a normalized form of bispectrum, it is independent of the signal energy or signal amplitude and can be used as a convenient test statistic for detecting non-Gaussian, nonlinear, and coupled processes [36], [44]- [46]. Aiming at the high computational cost and over-fitting possibility caused by directly using the bispectrum matrix or bicoherence matrix as a feature vector, the integrated bispectrum with low dimension, constant scaling, and translation is introduced in [47], including Radially Integrated Bispectrum (RIB), Cyclicly Integrated Bispectrum (CIB), Axially Integrated Bispectrum (AIB), and Bispectral-MFCC (BMFCC)…”

“…Bispectral symmetric region Keep advantages of bispectrum and reduce the amount of calculation. [38] skewness Quantitative deviation from symmetry [39]- [41] Bispectrum cepstrum Keep phase information after the cepstrum [42] Coherence of bispectrum Irrespective of the energy or amplitude of the signal [36], [43]- [46] Integral bispectrum Low dimension, scale, and translation invariance [47] Diagonal slice of bispectrum Diagonal slice of bispectrum Compress the amount of data and reduce the amount of calculation [48] Generalized diagonal slice of bispectrum Keep more information than the bispectrum diagonal slice [64], [65] The classification error problem caused by the uncertainty factor  in blind communication can be eliminated by the theoretical derivation of the normalized fourth-order cumulants.…”

“…In the higher-order domain, for feature extraction of underwater target radiation noise, [43] extracted features after bispectrum estimation of the target signal to obtain a low-dimensional feature vector and input an RBF neural network classifier as a way to suppress Gaussian noise and reduce the dimensionality of the feature space. [36], [45] extract the specific characteristics of the source using the dual bicoherence spectrum and feed these characteristics back to the neural network classifier to achieve the classification of ocean noise. Based on the effectiveness of bispectrum features being used for neural network classification, [50] combined higher-order cumulants and Hilbert transform for feature extraction to extract the ratio of instantaneous frequency, relative standard deviation, central frequency, average intensity, higher-order moment, and higher-order cumulants between adjacent eigenmode functions, which are extracted and input into a back propagation neural network to identify and classify two types of ship targets.…”

A GTCC-Based Underwater HMM Target Classifier with Fading Channel Compensation

“…The studies show that the neural network method could outperform all the other classical techniques. Mohankumar et al [27] highlight the feasibility of realizing an intelligent classifier for marine noise signals, with the help of artificial intelligence, using higher-order cepstral features. Jahangir et al [28] present a classification algorithm using hidden Markov models.…”