Here is what the page “Anomaly Detection Using Self-Supervised… | Nova AI Stream” contains. Discover how self-supervised learning is transforming underwater acoustic monitoring. Anomaly Detection Using Self-Supervised Learning | Interactive Tutorial with Dr. Spencer Bialek presents a novel machine-learning framework designed to detect instrument-related anomalies in hydrophone data collected from Ocean Networks Canada (ONC). This method tackles a major challenge in long-term ocean observing: identifying technical malfunctions and data quality issues in massive acoustic datasets where manual review is impossible and labelled failures are rare.
The system focuses on common instrumentation problems—including data gaps, sensitivity drift, tonal artifacts, and other hardware-related anomalies that affect data integrity. Using a two-stage machine learning process, hydrophone audio is converted into spectrograms for analysis. Expedition Highlights from the E/V Nautilus 2026 Season | Nautilus Live Romanche Fracture Zone East Dive 8 | SOI Divestream 978 The full version stays on the original page, which this listing opens directly.