Anomaly Detection Using Self-Supervised Learning | Interactive Tutorial with Dr. Spencer Bialek

Discover how self-supervised learning is transforming underwater acoustic monitoring. In this interactive tutorial, 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. 🔹 Sta