Cross-domain Sound Recognition for Efficient Underwater Data Analysis

TL;DR

This paper introduces a deep learning method that leverages aerial sound models to efficiently analyze underwater acoustic data, reducing labeling effort and achieving high recognition accuracy.

Contribution

It presents a novel cross-domain approach combining visualization and neural network training to improve underwater sound recognition with minimal manual labeling.

Findings

F1 score exceeded 84.3% in recognizing airgun sounds

Visualization techniques facilitate data clustering and labeling

Method reduces manual effort in underwater data analysis

Abstract

This paper presents a novel deep learning approach for analyzing massive underwater acoustic data by leveraging a model trained on a broad spectrum of non-underwater (aerial) sounds. Recognizing the challenge in labeling vast amounts of underwater data, we propose a two-fold methodology to accelerate this labor-intensive procedure. The first part of our approach involves PCA and UMAP visualization of the underwater data using the feature vectors of an aerial sound recognition model. This enables us to cluster the data in a two dimensional space and listen to points within these clusters to understand their defining characteristics. This innovative method simplifies the process of selecting candidate labels for further training. In the second part, we train a neural network model using both the selected underwater data and the non-underwater dataset. We conducted a quantitative analysis to measure the precision, recall, and F1 score of our model for recognizing airgun sounds, a common type of underwater sound. The F1 score achieved by our model exceeded 84.3%, demonstrating the effectiveness of our approach in analyzing underwater acoustic data. The methodology presented in this paper holds significant potential to reduce the amount of labor required in underwater data analysis and opens up new possibilities for further research in the field of cross-domain data analysis.