AquaSignal: An Integrated Framework for Robust Underwater Acoustic Analysis
Eirini Panteli, Paulo E. Santos, Nabil Humphrey

TL;DR
AquaSignal is a comprehensive deep learning framework that enhances underwater acoustic analysis by integrating denoising, classification, and novelty detection, demonstrating improved accuracy in real-world marine scenarios.
Contribution
This study introduces AquaSignal, the first integrated pipeline combining denoising, classification, and anomaly detection for underwater acoustic signals using deep learning.
Findings
Achieves 71% classification accuracy on underwater data
Attains 91% accuracy in novelty detection
Demonstrates robustness in noisy marine environments
Abstract
This paper presents AquaSignal, a modular and scalable pipeline for preprocessing, denoising, classification, and novelty detection of underwater acoustic signals. Designed to operate effectively in noisy and dynamic marine environments, AquaSignal integrates state-of-the-art deep learning architectures to enhance the reliability and accuracy of acoustic signal analysis. The system is evaluated on a combined dataset from the Deepship and Ocean Networks Canada (ONC) benchmarks, providing a diverse set of real-world underwater scenarios. AquaSignal employs a U-Net architecture for denoising, a ResNet18 convolutional neural network for classifying known acoustic events, and an AutoEncoder-based model for unsupervised detection of novel or anomalous signals. To our knowledge, this is the first comprehensive study to apply and evaluate this combination of techniques on maritime vessel…
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Taxonomy
TopicsUnderwater Acoustics Research · Underwater Vehicles and Communication Systems · Speech and Audio Processing
MethodsConcatenated Skip Connection · Max Pooling · Convolution · *Communicated@Fast*How Do I Communicate to Expedia? · Sparse Evolutionary Training · U-Net
