# Information Complexity of Time-Frequency Distributions of Signals in Detection and Classification Problems

## 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.

## Key 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.