This adorable sea lion has to eat five to eight percent of its body weight every day to stay healthy and hydrated. Breakthroughs, discoveries, and DIY tips sent six days a week. Over the long and complicated course of evolutionary history, mammals independently turned towards water to make a home multiple times. While many of the warm-blooded animals that abandoned dry land for a watery habitat no longer exist, we still have plenty of stunning examples: Think dolphins, whales, manatees, porpoises. There's even a whole suborder of carnivores called the pinnipeds, which includes seals, sea lions, and walruses who move between land and water.

We propose a methodology for training foundation models that enhances their in-context learning capabilities within the domain of bioacoustic signal processing. We use synthetically generated training data, introducing a domain-randomization-based pipeline that constructs diverse acoustic scenes with temporally strong labels. We generate over 8.8 thousand hours of strongly-labeled audio and train a query-by-example, transformer-based model to perform few-shot bioacoustic sound event detection. Our second contribution is a public benchmark of 13 diverse few-shot bioacoustics tasks. Our model outperforms previously published methods by 49%, and we demonstrate that this is due to both model design and data scale. We make our trained model available via an API, to provide ecologists and ethologists with a training-free tool for bioacoustic sound event detection.

Maritime transportation is paramount in achieving global economic growth, entailing concurrent ecological obligations in sustainability and safeguarding endangered marine species, most notably preserving large whale populations. In this regard, the Automatic Identification System (AIS) data plays a significant role by offering real-time streaming data on vessel movement, allowing enhanced traffic monitoring. This study explores using AIS data to prevent vessel-to-whale collisions by forecasting long-term vessel trajectories from engineered AIS data sequences. For such a task, we have developed an encoder-decoder model architecture using Bidirectional Long Short-Term Memory Networks (Bi-LSTM) to predict the next 12 hours of vessel trajectories using 1 to 3 hours of AIS data as input. We feed the model with probabilistic features engineered from historical AIS data that refer to each trajectory's potential route and destination. The model then predicts the vessel's trajectory, considering these additional features by leveraging convolutional layers for spatial feature learning and a position-aware attention mechanism that increases the importance of recent timesteps of a sequence during temporal feature learning. The probabilistic features have an F1 Score of approximately 85% and 75% for each feature type, respectively, demonstrating their effectiveness in augmenting information to the neural network. We test our model on the Gulf of St. Lawrence, a region known to be the habitat of North Atlantic Right Whales (NARW). Our model achieved a high R2 score of over 98% using various techniques and features. It stands out among other approaches as it can make complex decisions during turnings and path selection. Our study highlights the potential of data engineering and trajectory forecasting models for marine life species preservation.

One of the world's most endangered whales was spotted swimming with a newborn in the waters off South Carolina's Hilton Head island. A drone camera captured footage of a 50-ton North Atlantic right whale and her calf frolicking about four miles from shore. According to the boat captain who spotted the pair on Friday, the mother was 50 feet long and the calf was close to 15 feet. The North Atlantic right whale is among the rarest of marine mammals, with less than 400 left in the world. Collisions with boats and entanglement in lobster nets are the main reason they are critically endangered.