The ship is now being asked to file its own wake signature
the ship is now being asked to file its own wake signature: a bioacoustic foundation model trained mostly on bird sounds isolates boat engine noise across thousands of hours of reef recordings, a weakly supervised detector on a 120 kilometre subsea cable finds dark vessels that switch off their transponders, and a four dataset benchmark shows ship noise recognition fails to generalize across archives.
the harbor now files its own acoustic log.
Context
A cross-dataset benchmark of 11 September 2026 (When Underwater Acoustic Recognition Fails Across Datasets) benchmarks four ship-radiated-noise corpora with ImageNet-pretrained ResNet-18 classifiers and reports that direct transfer fails across all twelve cross-library directions, with accuracies of 0.0 to 47.9 percent below majority-class chance levels of 55.6 to 88.9 percent. Google Research's post of 9 February 2026 on AI trained on birds for underwater bioacoustics evaluates Perch 2.0 on three underwater datasets including ReefSet. An arXiv paper, Sea-Scan, describes weakly supervised dark vessel detection and localisation from distributed acoustic sensing on a subsea fibre link.
The four-dataset finding is a separate paper from the other two. The bird-trained model is a transfer-learning result on classification of underwater sounds, and the text read does not state that it isolates boat engine noise across thousands of hours of reef recordings. The 120 kilometre subsea cable was not found in the text read for Sea-Scan, so the cable length is unverified, and the claim of finding vessels that switch off their transponders matches its dark vessel framing. The harbor now files its own acoustic log is the author's framing.
Related work
- Earlier note in this series: the river ↗Same pattern: a natural system forecast by learned models.
Watch next
- The reef noise study behind the isolating boat noise claim and the cable length for Sea-Scan.
Sources
Provenance
The note above is reproduced unedited from the original post, first published on Threads on 4 October 2026 at 19:09 IST. Sources are the papers and datasets the note draws on.
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