Visual observations of the marine biodiversity can be difficult in specific areas for different reasons, including weather conditions or a lack of observers. In such conditions, passive acoustics represents a potential alternative approach. The objective of this work is to demonstrate how information about marine biodiversity can be obtained via detailed analysis of the underwater acoustic environment. This paper presents the first analysis of the Saint-Pierre-and-Miquelon (SPM) archipelago underwater acoustic environment. In order to have a better knowledge about the marine biodiversity of SPM, acoustic recordings were sampled at different time periods to highlight seasonal variations over several years. To extract information from these acoustic recordings, standard soundscape and ecoacoustic analysis workflow was used to compute acoustic metrics such as power spectral density, third-octave levels, acoustic complexity index, and sound pressure levels. The SPM marine acoustic environment can be divided into three main sound source classes: biophony, anthrophony, and geophony. Several cetacean species were encountered in the audio recordings including sperm whales (which were detected by visual observations and strandings of 3 males in 2014), humpback, and blue whales.
Cetacean Distribution Modeling (CDM) is used to quantify mobile marine species distributions and densities. It is essential to better understand and protect whales and their relatives. Current CDM approaches often fail in capturing general species-environment relationships, which would be valid within a broader range of environmental conditions that characterize the surveyed regions. This paper aims at investigating the usefulness of deep learning based schemes, namely multi-task and transfer learning, in CDM. Co-training of a stochastic presence-background model on a classification task and a deterministic rulebased model on a regression task was performed. Whale presence-only records were used for the first task, and index outputs of a feeding habitat occurrence model for the second one. This new approach has been experimented through the study case of fin whales in the western Mediterranean Sea. To evaluate our approach, a new metric called True Positive rate per unit of Surface Habitat (TPSH) and an original multimodal fully-connected neural networks were developed. A Generalized Additive Model (GAM)-a standard CDM method-was also used as a reference for performance. Results show that our multi-task learning model improves both the feeding habitat model by 10.8% and data-driven models such as GAM by 16.5% on our TPSH metric in relative terms, revealing a higher accuracy of our approach in estimating whale presence. Such trends in results have been further supported by the use of two other independent datasets that forced models to generalize beyond their training dataset of species-environment relationships. Performance could be further improved by adopting more optimal thresholds as observed from Receiver Operating Characteristic curves, e.g. the multi-task learning model could reach absolute gains up to 10% in the median of the True Positive Rate while maintaining its habitat spatial spreading. Globally, our work confirmed our working hypothesis that expert information on whale behaviour represent a good knowledge base for model generalization. This result can be further improved by a concurrent learning of more local species-environment relationships from in-situ presence data.
In recent years, deep neural networks have been successfully applied to solve a range of detection and classification tasks in underwater acoustics, outperforming existing methods. However, deep learning models are “data hungry” requiring large amounts of accurately labelled acoustic samples to train. Moreover, a certain amount of “fine tuning” is often required to achieve satisfactory performance in a new acoustic environment. Thus, the development of deep learning models depends on the input of expert human analysts, both for building the initial training set and for adjusting the model's performance. However, open-source software to facilitate this collaboration between machine learning developers and acousticians is currently lacking. To address this need, our team is building a web-based application for collaboratively annotating sound samples and validating model predictions. The user interface is designed to be familiar to acousticians, while machine learning developers have access to a dashboard allowing them to efficiently leverage the acousticians' expert knowledge. In this contribution, an overview of the application will be given and its functionalities will bedemonstrated through its application to the HALLO (Humans and ALgorithms Listening for Orcas) project. Future developments will also be described, highlighting complementary applications under development such as a model adaptation tool.
Shipping traffic continues to grow in the Salish Sea with considerable marine industrial developments planned for the region. With this comes an increase in the risk of cetaceans being disturbed, harassed, and potentially colliding with commercially operating vessels. One key conservation concern is these waters coincide with designated Critical Habitat for the endangered population of Southern Resident killer whales (SRKW). Monitoring technologies such as hydrophones provide the promise of real-time animal detection and localisation. To use these continuous streams of real-time whale location data, we are developing algorithms that automate acoustic detection and classification of SRKW calls using deep learning models trained on extensive new and updated annotated datasets from the region. We are developing open-source models for pod-level classification, while also differentiating SRKW from other ecotypes and species. We are developing a forecasting system to merge these whale detections with opportunistic visual observations. This is based on sequential data assimilation methods using advanced animal movement models to estimate SRKW pod locations with probabilistic predictions of whale directional movement. These methods will be transformative for shipping by providing forecasting with a lead time of a few hours allowing vessels to adjust their speed or pathway to minimize whale-vessel interactions.
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