Passive acoustic monitoring (PAM) is an effective and widely used tool for tracking marine mammals, particularly beaked whales, which are infrequently observed visually because of their deep-diving behavior. However, the large data sets generated by PAM methods often require time-consuming hand labeling to identify whale trajectories in the recorded audio. Automated multi-target tracking (MTT) methods could significantly reduce human workload, but current methods have difficulty forming continuous tracks because of the irregularity of beaked whale echolocation clicks. More precisely, regular sequences of clicks are often interrupted by longer pauses that occur when whales face away from the sensors or stop clicking. Consequently, the probability of detection is difficult to model accurately, and MTT trajectories become fragmented at these pauses. In this paper, we propose a multistage target-estimation method aimed at bridging large gaps in click sequences by combining belief propagation-based MTT with track smoothing and stitching. We validate our method using acoustic recordings of clicks from goose-beaked whales (Ziphius cavirostris), and demonstrate that it improves track estimates and reduces fragmentation in the presence of consecutive missed detections. When evaluated with the generalized optimal subpattern assignment (GOSPA) metric, our method outperforms existing MTT reference methods through reductions in missed-target errors.
Passive acoustic monitoring of killer whales is particularly important for conservation of the endangered Southern Resident killer whale population, but requires accurate models that can operate in real time under severe class imbalance and deployment shift. We propose a lightweight ResNet-based two-stage cascade that first detects killer whale vocalizations and then classifies confident detections into five eastern North Pacific ecotypes, abstaining on ambiguous calls. We train and evaluate the pipeline on the DCLDE 2027 dataset, where the detector achieves 0.960 macro-F1 and the classifier 0.958, outperforming frozen Perch 2.0 embeddings on the five-ecotype benchmark. By separating detection from ecotype recognition, the end-to-end cascade improves seven-class macro-F1 from 0.919 for a single-stage model to 0.933, with the largest gain on the rare OKW ecotype. To assess transfer beyond the benchmark, we use active learning to adapt the Stage 1 to the acoustic environment of Puget Sound, WA, increasing killer whale detection F1 from 0.405 to 0.755 on manually verified detection windows. Finally, each stage processes a 3 s window in approximately 1.4 ms on an NVIDIA H100, enabling faster than real time inference. These results demonstrate that the proposed two-stage cascade pipeline enables reliable killer whale detection and classification, adaptation to new acoustic domains, and real-time monitoring for conservation applications.
Passive Acoustic Monitoring (PAM) is an efficient and non-invasive method for surveying ecosystems at a reduced cost. Typically, autonomous recorders allow the acquisition of vast bioacoustic datasets which are then analyzed. However, power consumption and data storage are both scarce and limit the duration of acquisition campaigns. To address this issue, we propose a smart PAM system which allows the in-situ analysis of the soundscape by embedding a classifier directly onto an AudioMoth microcontroller. Specifically, we propose an optimized yet simple 1D Convolutional Neural Network (1D-CNN) to classify the raw audio. The model focuses on the specific call of Scopoli Shearwater seabirds (endangered species) and is trained on a real-world dataset with a classification accuracy of 91% (balanced accuracy of 89%). We also propose a process to optimize the model to fit the severe resource constraints of the AudioMoth, achieving a ~10kB RAM memory footprint and 20ms inference time. Finally, we present an open-source tutorial of our model optimization and export strategy which can be used for embedding models beyond the scope of our study. Our modified version of the AudioMoth firmware adds two functions: (F1) which selectively records data when the target species has been detected and (F2) which logs the continuous classification results in real time. This work intends to facilitate the conception of intelligent sensors, enhancing the efficiency and scalability of bioacoustic monitoring campaigns.
Louis Lerbourg, Paul Peyret, Juliette Linossier +1
Bioacoustic data from Passive Acoustic Monitoring (PAM) generates large datasets where obtaining detailed auditing and labelling is often impractical, resulting in weak annotations (e.g., presence/absence of species over several minutes of recording). In order to effectively capture the complex temporal patterns and key features of long audio segments, we propose a framework comprising dataset standardisation, feature extraction, and classification via Temporal Convolutional Networks (TCN). This approach eliminates the necessity for setting heuristic decision rules or creating time-consuming strong labels. To demonstrate the effectiveness of our approach, we use sperm whale (\textit{Physeter macrocephalus}) click trains in 4-minute recordings as a case study, from a dataset comprising diverse sources and deployment conditions to maximise generalisability. Our TCN classifiers achieve recall rates exceeding 0.83 at a 0.13 false positive rate, comparable to agreement rates between expert annotators. We compare two methods of feature extraction, Variational AutoEncoders (VAEs) and traditional handpicking of features, and found them to yield similar performance results, with the VAE-based classifiers seeing a more stable performance across datasets and recording conditions. These results offer a way forward in leveraging numerous existing annotated bioacoustic datasets to train automatic classification models, effectively overcoming previous limitations associated with weak labels.
Laia Garrobé Fonollosa, Douglas Gillespie, Lina Stankovic +2