AI Methods for Fisheries Acoustics: From Supervised Classification to Foundation Model

This presentation provides an overview of our recent work on artificial intelligence (AI) methods for fisheries acoustic data.
Our initial work focused on acoustic target classification of Sandeel using CNNs. These models achieved strong performance. However, they required precise labels for training and most survey series lack the precise labels available for Sandeel. Often, the data is weak-ly labelled, where portions of energy in large areas are assigned to an acoustic category. We have developed methods for processing these weak labels to make them suitable for train-ing deep learning models.
We are also developing and training a foundation model (a large, general-purpose model that can be adapted to many tasks) for acoustic data using a self-supervised approach. The model will be pre-trained on a large, unlabelled dataset. During this pre-training, the mod-el learns general patterns and structures in the data. Once pre-trained, the model can be adapted to a variety of tasks, such as acoustic target classification of multiple species, using only small amounts of labelled data.
Key challenges in this work include the computational demands and large dataset required for effective pre-training, and the critical role of data sampling. We are developing sam-pling strategies using existing large-scale AI models and statistical features of the data, to ensure the model receives enough samples containing acoustic targets during pre-training.
Finally, inference code for trained models is containerized and made available as separate tasks for data processing flows, such as the Kongsberg Blue Insight system and the IMR data platforms.