Publications and research artefacts

Open-Set Vessel Re-Identification from Underwater Ship-Radiated Noise with a Raw-Waveform Selective-Kernel Acoustic Neural Network (SKANN) and a Cross-Passage Evaluation Protocol

Formalises open-set, cross-passage vessel re-identification on public hydrophone data; specifies an evaluation protocol with hull-disjoint splits, cross-passage galleries and audio-adjudicated transit deduplication; describes SKANN, a raw-waveform encoder trained with an angular-margin objective; and reports results on IARA, ShipsEar and a cross-network experiment that support analyst triage over a ranked shortlist rather than identification.

Tyagi S. arXiv:2609.07399 [eess.AS], September 2026 · arXiv · doi:10.48550/arXiv.2609.07399 · Author affiliation: Oravont Systems LLP, Noida · ORCID 0000-0001-5897-7955

System and method for open-set re-identification of individual vessels from underwater ship-radiated noise using a raw-waveform selective-kernel acoustic neural network, and a cross-passage evaluation protocol therefor

Covers the raw-waveform selective-kernel encoder that builds a hull-identity embedding through multi-resolution processing; the matching of a recording against an enrolled gallery by cosine similarity, with rejection of hulls that are not in it; and the leakage-resistant cross-passage evaluation protocol with transit-level deduplication of source recordings.

Indian Patent Office, provisional application No. 202611107132, filed 6 September 2026 · Applicant: Oravont Systems LLP · Inventor: Sunil Tyagi · Complete specification pending.

Graphical abstract: raw waveform to hull-identity embedding, gallery match or rejection, and the evaluation protocol that keeps the measurement leakage-free.

SKANN model checkpoint, validation embeddings, transit map and per-query evaluation outputs

The companion record to the preprint: the model checkpoint with SHA-256 digests, validation embeddings, the transit-deduplication map and per-query outputs, so that every reported number can be recomputed. Source corpora are credited in the record: IARA (CC BY-NC 4.0), ShipsEar (distributed by the Universidad de Vigo on request) and Ocean Networks Canada, Oceans 3.0.

Zenodo · version DOI 10.5281/zenodo.22160138 · concept DOI 10.5281/zenodo.22160137 · CC BY 4.0, with an MIT carve-out for the adjudicator script · Patent notice included.

A Physics-Grounded Synthetic Underwater Acoustic Dataset for Underwater Domain Awareness

Full-factorial waveform generation from first-principle acoustic models: 12,000 physics-grounded clips across four vessel classes and ambient ocean noise, with corrected ambient-noise modelling, physics-based cavitation and worked verification calculations, released for self-supervised pretraining.

Oravont Systems LLP, 2026 · GitHub repository (CC BY 4.0) · Article · Also on LinkedIn

DEMON Analysis: Reading a Vessel's Propulsion from Its Own Noise

A practical walk-through of Detection of Envelope Modulation on Noise: what it extracts, the pipeline (bandpass → Hilbert envelope → AC-coupling → envelope FFT), how to read the harmonic comb, the band-selection decision most tutorials skip, and a case study in which DEMON exposed a defect in a synthetic dataset that conventional spectrograms had hidden. The toolkit that produced the figures is public.

Tyagi S. Working note, Oravont Systems LLP, 2026 · github.com/Oravont/DEMON-Analysis (code MIT; documentation, figures and samples CC BY 4.0) · Full note (PDF) · Also on LinkedIn

Deep Learning for Underwater Domain Awareness — capability overview

Passive-sonar acoustic intelligence at an unclassified, public level: the DEMON toolkit, the SKANN architecture, the PS12 detection-and-classification system and the DISC5 re-identification work, with the deployment principles behind them.

Oravont Systems LLP, 2026 · PDF