Information Complexity of Time-Frequency Distributions of Signals in Detection and Classification Problems
Pavel Lysenko, Andrey Galyaev, Leonid Berlin, Vladimir Babikov
TL;DR
This paper introduces new information-based features for analyzing acoustic signals, showing high accuracy in classification using machine learning.
Contribution
Proposes novel information features based on time-frequency distributions for signal classification.
Findings
New features based on spectrogram and reassigned spectrogram improve classification performance.
High F1 score of 0.95 demonstrates the effectiveness of the proposed features.
Results validated using synthetic and real hydroacoustic data.
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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Taxonomy
TopicsFault Detection and Control Systems · Neural Networks and Applications · Advanced Research in Systems and Signal Processing