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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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Related Experiment Video

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Impact of data augmentation on supervised learning for a moving mid-frequency source.

J A Castro-Correa¹, M Badiey¹, T B Neilsen²

¹Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware 19716, USA.

The Journal of the Acoustical Society of America

|December 2, 2021

Summary

Deep learning models accurately locate underwater sound sources and classify seabed types. Data augmentation significantly improved model performance for both simulated and real-world oceanographic data.

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Area of Science:

Ocean acoustics
Machine learning
Signal processing

Background:

Accurate source localization and seabed classification are crucial for underwater acoustic monitoring.
Traditional methods face challenges with complex environmental variations.

Purpose of the Study:

To develop and evaluate deep learning models for underwater source localization and seabed classification.
To assess the impact of data augmentation on model performance.

Main Methods:

Implemented two residual networks: one for seabed classification and another for source localization.
Trained models using synthetic data generated by the ORCA normal mode model.
Tested models on measured and simulated data, incorporating variations in sound speed profiles and seabed properties.
Applied nine data augmentation techniques to enhance model robustness.

Main Results:

Achieved consistent source localization estimation.
Obtained seabed classification accuracy above 65% even in worst-case scenarios.
Identified complex data augmentation techniques like time warping and masking as key drivers for performance improvement.

Conclusions:

Residual networks offer a viable approach for underwater acoustic source localization and seabed classification.
Data augmentation is essential for improving the generalization and accuracy of these models in diverse ocean environments.