ArticleEntropy (Basel, Switzerland)2025
Information Complexity of Time-Frequency Distributions of Signals in Detection and Classification Problems.
Article in Entropy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- TF-STNet: A Time-Frequency Dual-Branch Spatiotemporal Network for NWP-to-Station Bias Correction.Entropy (Basel, Switzerland) · 2026Article
- Spectral Entropy Analysis and Source-Level EMI Suppression in Inverters via Sequential Switching of Series-Connected IGBTs.Entropy (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The paper considers the problem of detecting and classifying acoustic signals based on information (entropy) criteria. A number of new information features based on time-frequency distributions are proposed, which include the spectrogram and its upgraded version, the reassigned spectrogram. To confirm and verify the proposed characteristics, modeling on synthetic signals and numerical verification of the solution of the multiclass classification problem based on machine learning methods on real hydroacoustic recordings are carried out. The obtained high classification results (F1=0.95) allow us to assert the advantages of using the proposed characteristics.
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Registered trials
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