Biology subjects

Lostanlen, V.

Publications and source records attributed to Lostanlen, V..

3 recordsLinked to original sources

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

The reliability of acoustic classification models for determining avian vocalisation patterns

Automated detection and classification of species vocalisations offers the potential to utilise acoustic datasets across unprecedented spatial and temporal scales. However, classification algorithms inevitably generate errors, and error rates vary with context. While methods for quantifying error rates in ecoacoustics are well established, there is limited research on what level of model performance is sufficient to reliably determine avian vocalisation patterns. Using an extensive fully expert-labelled acoustic dataset from Peru (18 hours, 6 sites), we examined changes in the probability of detecting target bird vocalisations in the first hour after dawn to address three key questions: (1) How sensitive are models predicting detection probability over time to reductions in classification accuracy? (2) To what extent does aggregating detections over longer time periods impact classification accuracy? Additionally, we used the labelled dataset to assess how the creation and composition of a test dataset for assessing classifier performance can impact the reliability of accuracy metrics: (3) Are estimates of classification precision robust when test and deployment datasets are not independent and identically distributed? Our results indicate that poor classification performance--especially low precision--can lead to misleading inferences about temporal patterns of vocalisations. Aggregating classifier predictions over longer time periods improved recall but often resulted in misleading patterns of vocal behaviour by reducing precision and temporal resolution. We also demonstrate that precision can be substantially overestimated when species presences are rarer in the deployment dataset than in test data. These findings highlight the importance of cautious application of automated classification in acoustic ecology and the need for accuracy assessment methods tailored to the intended ecological analysis.

ecology↗

bioRxiv2022

BirdVox: Machine listening for bird migration monitoring

The steady decline of avian populations worldwide urgently calls for a cyber-physical system to monitor bird migration at the continental scale. Compared to other sources of information (radar and crowdsourced observations), bioacoustic sensor networks combine low latency with a high taxonomic specificity. However, the scarcity of flight calls in bioacoustic monitoring scenes (below 0.1% of total recording time) requires the automation of audio content analysis. In this article, we address the problem of scaling up the detection and classification of flight calls to a full-season dataset: 6672 hours across nine sensors, yielding around 480 million neural network predictions. Our proposed pipeline, BirdVox, combines multiple machine learning modules to produce per-species flight call counts. We evaluate BirdVox on an annotated subset of the full season (296 hours) and discuss the main sources of estimation error which are inherent to a real-world deployment: mechanical sensor failures, sensitivity to background noise, misdetection, and taxonomic confusion. After developing dedicated solutions to mitigate these sources of error, we demonstrate the usability of BirdVox by reporting a species-specific temporal estimate of flight call activity for the Swainsons Thrush (Catharus ustulatus).

ecology↗