Abstract
The scarcity of labeled underwater acoustic data limits the development of data-driven models for marine acoustic monitoring, motivating the use of self-supervised learning to exploit the abundance of unlabeled recordings. However, large-scale data alone does not guarantee effective representation learning, because model performance is strongly influenced by the distribution of the training data. To facilitate such training data, this study introduces a scalable curation pipeline that curates 3,000 hours from 41 years of raw NOAA hydrophone recordings, producing MarineSet, a large-scale and diverse dataset designed for underwater acoustic learning. A baseline model, MarineNet, trained on MarineSet achieves competitive performance on marine mammal call and ship-type classification, highlighting the value of systematic data curation for developing self-supervised learning models in underwater acoustics.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33080
