arXiv:2607.07733v1 Announce Type: cross Abstract: Passive hydroacoustic monitoring often generates large volumes of continuous recordings that are only partially exploited due to the cost of manual annotation. Supervised detection methods perform well but require large labeled datasets, seldom available for rare signals or understudied environments. This work proposes a self-supervised exploration pipeline to address this limitation in low-frequency settings. A Masked AutoEncoder (MAE) is pre-trained on a reconstruction pretext task, then used to extract patch-level representations from spectr
The proliferation of raw data in various scientific and industrial fields, combined with advances in self-supervised learning, makes the development of more efficient data exploitation methods critical.
This development has the potential to unlock insights from vast, previously under-utilized datasets, particularly in environmental monitoring and defense, without the prohibitive cost of manual annotation.
The barrier to effectively leveraging large continuous sensor data streams, especially for rare or complex signals, is lowered through reduced reliance on extensive labeled datasets.
- · Environmental monitoring organizations
- · Defense intelligence agencies
- · Machine learning researchers
- · Sensor manufacturers
- · Manual data annotation services (in specific niches)
More cost-effective and widespread deployment of passive hydroacoustic monitoring systems becomes feasible.
Improved detection and tracking of marine life, sub-surface activities, or geological events without human intervention accelerates.
New forms of automated, 'always-on' situational awareness emerge for oceans and other environments, impacting security and resource management.
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Read at arXiv cs.LG