Application of the 2D Local Entropy Information in Sparse TFD Reconstruction

Jurdana, Vedran; Volaric, Ivan; Sucic, Victor

doi:10.1109/cobcom55489.2022.9880775

Cited by 2 publications

(3 citation statements)

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“…The utility of the iterative LRE is better suited for purposes such as extracting the strongest component, as demonstrated in [6], and for optimization purposes where cross-terms need to be detected, interpreting NC iter t as the number of total energy regions rather than the number of auto-terms [17]. Therefore, in this study, we opt to consider the original and more robust LRE method for comparison in Section 3, as has been widely utilized across various applications [11,[18][19][20][21][22]39]. (f) NC t , ⌊NC iter t ⌉ and ⌊NC t ⌉ corresponding to the LOADTFD in (c).…”

Section: Limitationsmentioning

confidence: 99%

“…Finally, we embedded randomly selected signals into AWGN with SNR down to 0 dB. We selected EMB, SPWV, LOADTFD, and WVD as the training data TFDs, primarily for their widespread application in the LRE method as documented in previous studies [11,16,[18][19][20][21][22]39]. The inclusion of these TFDs facilitates a comprehensive comparison with the LRE method.…”

Section: Training Setmentioning

confidence: 99%

“…Conversely, smaller window sizes may emphasize unresolved cross-terms or noise samples, leading to reduced accuracy in estimated numbers and poorer curve smoothness [24]. Although local component numbers are typically rounded for practical applications [18][19][20][21]39,54], raw output from the proposed CNN may limit its usage in cases where the identification of local maxima is required. Therefore, our future research will explore refining the CNN's raw output through careful analysis of smoothing filters and methods while preserving its dynamic capabilities.…”

Section: Limitations Of the Proposed Approachmentioning

confidence: 99%

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Convolutional Neural Networks for Local Component Number Estimation from Time–Frequency Distributions of Multicomponent Nonstationary Signals

Jurdana,

Baressi Šegota

2024

Mathematics

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Frequency-modulated (FM) signals, prevalent across various applied disciplines, exhibit time-dependent frequencies and a multicomponent nature necessitating the utilization of time-frequency methods. Accurately determining the number of components in such signals is crucial for various applications reliant on this metric. However, this poses a challenge, particularly amidst interfering components of varying amplitudes in noisy environments. While the localized Rényi entropy (LRE) method is effective for component counting, its accuracy significantly diminishes when analyzing signals with intersecting components, components that deviate from the time axis, and components with different amplitudes. This paper addresses these limitations and proposes a convolutional neural network-based (CNN) approach for determining the local number of components using a time–frequency distribution of a signal as input. A comprehensive training set comprising single and multicomponent linear and quadratic FM components with diverse time and frequency supports has been constructed, emphasizing special cases of noisy signals with intersecting components and differing amplitudes. The results demonstrate that the estimated component numbers outperform those obtained using the LRE method for considered noisy multicomponent synthetic signals. Furthermore, we validate the efficacy of the proposed CNN approach on real-world gravitational and electroencephalogram signals, underscoring its robustness and applicability across different signal types and conditions.

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