Investigation of Time-Frequency Feature Combinations with Histogram Layer Time Delay Neural Networks
Abstract
While deep learning has reduced the prevalence of manual feature extraction, transformation of data via feature engineering remains essential for improving model performance, particularly for underwater acoustic signals. The methods by which audio signals are converted into time-frequency representations and the subsequent handling of these spectrograms can significantly impact performance. This work demonstrates the performance impact of using different combinations of time-frequency features in a histogram layer time delay neural network. An optimal set of features is identified with results indicating that specific feature combinations outperform single data features.
Keywords
Cite
@article{arxiv.2409.13881,
title = {Investigation of Time-Frequency Feature Combinations with Histogram Layer Time Delay Neural Networks},
author = {Amirmohammad Mohammadi and Iren'e Masabarakiza and Ethan Barnes and Davelle Carreiro and Alexandra Van Dine and Joshua Peeples},
journal= {arXiv preprint arXiv:2409.13881},
year = {2025}
}
Comments
6 pages, 4 figures. This work has been submitted to the IEEE for possible publication. This work has been accepted to IEEE OCEANS 2025