Biology subjects

Stowell, D.

Publications and source records attributed to Stowell, D..

5 recordsLinked to original sources

bioRxiv2026

Detection-Guided Beamforming for Efficient Bat Localisation

Passive acoustic monitoring is widely used to study wildlife, but current approaches provide limited insight into the spatial behaviour of animals. In bat ecology, reconstructing flight trajectories is essential for studying habitat use, movement patterns, and interactions, yet it remains difficult to achieve under field conditions. Acoustic cameras offer a potential solution by enabling sound source localisation, but their practical application is limited by the computational cost of beamforming and by the non-stationary, broadband, and transient nature of echolocation calls. In particular, exhaustive beamforming over wide ultrasonic bandwidths and dense spatial grids becomes infeasible for continuous monitoring. In this work, we propose a detection-guided beamforming framework for efficient localisation of free-flying bats. The method exploits the sparsity of echolocation signals by restricting beamforming to detector-identified time-frequency regions and combines this with physically consistent short-time analysis parameters and dense spatial sampling. The framework is evaluated on field recordings acquired with a Sorama CAM iV64s acoustic camera. Results show that detection-guided processing reduces the number of beamformer evaluations by approximately 77 times, corresponding to a reduction of about 98.7 % in computational effort, while preserving spatial resolution. At the same time, the proposed parameter configuration improves the stability and sharpness of reconstructed trajectories by avoiding artefacts associated with temporal averaging. These findings demonstrate that high-resolution acoustic localisation can be achieved under realistic computational constraints, supporting the integration of acoustic cameras into ecological monitoring workflows. The proposed framework enables the extraction of spatial trajectories from passive acoustic monitoring data, facilitating spatially resolved analyses of bat behaviour in field conditions.

bioinformatics↗

bioRxiv2026

BioDCASE: Using data challenges to make community advances in computational bioacoustics

Computational bioacoustics has seen significant advances in recent decades. However, the rate of insights from automated analysis of bioacoustic audio lags behind our rate of collecting the data - due to key capacity constraints in data annotation and bioacoustic algorithm development. Gaps in analysis methodology persist: not because they are intractable, but because of resource limitations in the bioacoustics community. To bridge these gaps, we advocate the open science method of data challenges, structured as public contests. We conducted a bioacoustics data challenge named BioDCASE, within the format of an existing event (DCASE). In this work we report on the procedures needed to select and then conduct useful bioacoustics data challenges. We consider aspects of task design such as dataset curation, annotation, and evaluation metrics. We report the three tasks included in BioDCASE 2025 and the resulting progress made. Based on this we make recommendations for open community initiatives in computational bioacoustics.

animal behavior and cognition↗

bioRxiv2025

Data-driven Sampling Strategies for Fine-Tuning Bird Detection Models

Passive Acoustic Monitoring has emerged as a promising tool for collecting ecological data, particularly in the context of bird population monitoring. Bird species can be automatically identified using pre-trained models, such as BirdNET. The performance of these models can be significantly improved through fine-tuning with annotated samples recorded in the specific acoustic conditions in which the microphones are deployed. However, PAM collects vast amounts of data, and annotating bird vocalizations requires specialized expetise. As a result, only a very small portion of the recordings can be effectively labeled. Selecting the most relevant samples to annotate in order to maximize performance in model fine-tuning remains a significant challenge. First, a regularization technique addresses the challenge of class imbalance during model fine-tuning. Next, a data-driven methodology is developed, introducing the influence score, which quantifies the impact of individual training samples on model performance to inform sampling strategies. A linear model is proposed to estimate the influence score for generalization to unseen data. Finally, several sampling strategies are compared, based on acoustic indices and predictions of the pre-trained model. Together, these contributions enable the identification of efficient annotation strategies to overcome the challenges of limited annotation resources in large-scale passive acoustic monitoring.

bioinformatics↗

bioRxiv2025

Stratified Active Learning for Spatiotemporal Generalisation in Large-Scale Bioacoustic Monitoring

Active learning optimises machine learning model training through the data-efficient selection of informative samples for annotation and training. In the context of biodiversity monitoring using passive acoustic monitoring, active learning offers a promising strategy to reduce the fundamental annotation bottleneck and improve global training efficiency. However, the generalisability of model performance across ecologically relevant strata (e.g. sites, season etc) is often overlooked. As passive acoustic monitoring is extended to larger scales and finer resolutions, inter-strata spatiotemporal variability also increases. We introduce and investigate the concept of stratified active learning to achieve reliable and generalisable model performance across deployment conditions. We compare between implicit cluster-based diversification methods and explicit stratification, demonstrating that cross-strata generalisation is a function of stratum divergence, not sampling balance. Additionally, mutual information as well as exclusion analysis show that spatiotemporal context can explain a substantial proportion of species label variance and inform sampling decisions.

bioinformatics↗

bioRxiv2022

Bird song comparison using deep learning trained from avian perceptual judgments

Our understanding of bird song, a model system for animal communication and the neurobiology of learning, depends critically on making reliable, validated comparisons between the complex multidimensional syllables that are used in songs. However, most assessments of song similarity are based on human inspection of spectrograms, or computational methods developed from human intuitions. Using a novel automated operant conditioning system, we collected a large corpus of zebra finches (Taeniopygia guttata) decisions about song syllable similarity. We use this dataset to compare and externally validate similarity algorithms in widely-used publicly available software (Raven, Sound Analysis Pro, Luscinia). Although these methods all perform better than chance, they do not closely emulate the avian assessments. We then introduce a novel deep learning method that can produce perceptual similarity judgements trained on such avian decisions. We find that this new method outperforms the established methods in accuracy and more closely approaches the avian assessments. Inconsistent (hence ambiguous) decisions are a common occurrence in animal behavioural data; we show that a modification of the deep learning training that accommodates these leads to the strongest performance. We argue this approach is the best way to validate methods to compare song similarity, that our dataset can be used to validate novel methods, and that the general approach can easily be extended to other species.

animal behavior and cognition↗