arXiv subjects

Hervé Glotin

Publications and source records attributed to Hervé Glotin.

11 recordsLinked to original sources

arXiv2026

Audiovisual diarization of overlapping click trains in sperm whale (Physeter macrocephalus) vocal sparring using a three-hydrophone array

The individual attribution of sperm whale (Physeter macrocephalus) vocalizations during surface interactions constitutes a methodological challenge due to acoustic overlaps, multipath propagation, body shadowing, and indistinguishable inter-pulse intervals among similar-sized individuals. A multimodal audio-visual workflow is presented to deinterleave and attribute uncharacterized click trains produced by immature males during ``vocal sparring'' using a portable three-hydrophone array coupled with synchronized video. The approach combines spectro-temporal tracking, based on inter-click interval dynamics and the Constant-Q Transform, with spatial time-difference-of-arrival modeling projected onto the image plane for optical validation. Analysis of 2,655 manually validated clicks shows that purely acoustic clustering diarization errors remain low, peaking at 25.79% only during extreme temporal superpositions. Integrating the optical modality resolves residual spatial indeterminacies; although near-planar array geometry induces vertical ambiguities, the system achieves up to 100% horizontal visual concordance for primary emitters. Crucially, this framework successfully reconstructed and assigned 11 distinct, intertwined click trains totaling 882 clicks to specific focal individuals despite near-field tactile constraints. Because ethological descriptions remain incomplete without identifying the emitter, explicitly correlating these emissions with physical kinematics provides the fine-scale resolution required to define this socio-acoustic behavior.

q-bio.QM↗

arXiv2023

A DNN Framework for Learning Lagrangian Drift With Uncertainty

Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data. Uncertainty is usually overcome by introducing stochasticity into the drift, but this approach requires specific assumptions for modelling uncertainty. We remove this constraint by presenting a purely data-driven framework for modelling probabilistic drift in flexible environments. Using ocean circulation model simulations, we generate probabilistic trajectories of object location by simulating uncertainty in the initial object position. We train an emulator of probabilistic drift over one day given perfectly known velocities and observe good agreement with numerical simulations. Several loss functions are tested. Then, we strain our framework by training models where the input information is imperfect. On these harder scenarios, we observe reasonable predictions although the effects of data drift become noticeable when evaluating the models against unseen flow scenarios.

cs.LG↗

arXiv2022

The bioacoustic proof of the effects of raising awareness of noise pollution among visitors to the Port Cros National Park using binding communication

Assuming that the anthropogenic impact of visitors to a natural park can be reduced by communication actions, we have drawn up a bioacoustical protocol combined with a protocol to measure the effectiveness of the communication actions. A simple eco-awareness communication method will be compared to a binding communication method. We address the questions: does visitors' eco-awareness drive them to change their attitudes and behavior in order to limit their own noise disturbance, and does commitment improve their awareness? We detail here the main concepts considered and the methodology followed to discuss the preliminary results.

eess.SP↗

arXiv2018

NIPS4Bplus: a richly annotated birdsong audio dataset

Recent advances in birdsong detection and classification have approached a limit due to the lack of fully annotated recordings. In this paper, we present NIPS4Bplus, the first richly annotated birdsong audio dataset, that is comprised of recordings containing bird vocalisations along with their active species tags plus the temporal annotations acquired for them. Statistical information about the recordings, their species specific tags and their temporal annotations are presented along with example uses. NIPS4Bplus could be used in various ecoacoustic tasks, such as training models for bird population monitoring, species classification, birdsong vocalisation detection and classification.

cs.SD↗

arXiv2018

Dirichlet Process Parsimonious Mixtures for clustering

The parsimonious Gaussian mixture models, which exploit an eigenvalue decomposition of the group covariance matrices of the Gaussian mixture, have shown their success in particular in cluster analysis. Their estimation is in general performed by maximum likelihood estimation and has also been considered from a parametric Bayesian prospective. We propose new Dirichlet Process Parsimonious mixtures (DPPM) which represent a Bayesian nonparametric formulation of these parsimonious Gaussian mixture models. The proposed DPPM models are Bayesian nonparametric parsimonious mixture models that allow to simultaneously infer the model parameters, the optimal number of mixture components and the optimal parsimonious mixture structure from the data. We develop a Gibbs sampling technique for maximum a posteriori (MAP) estimation of the developed DPMM models and provide a Bayesian model selection framework by using Bayes factors. We apply them to cluster simulated data and real data sets, and compare them to the standard parsimonious mixture models. The obtained results highlight the effectiveness of the proposed nonparametric parsimonious mixture models as a good nonparametric alternative for the parametric parsimonious models.

stat.ML↗

arXiv2018

Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge

Assessing the presence and abundance of birds is important for monitoring specific species as well as overall ecosystem health. Many birds are most readily detected by their sounds, and thus passive acoustic monitoring is highly appropriate. Yet acoustic monitoring is often held back by practical limitations such as the need for manual configuration, reliance on example sound libraries, low accuracy, low robustness, and limited ability to generalise to novel acoustic conditions. Here we report outcomes from a collaborative data challenge showing that with modern machine learning including deep learning, general-purpose acoustic bird detection can achieve very high retrieval rates in remote monitoring data --- with no manual recalibration, and no pre-training of the detector for the target species or the acoustic conditions in the target environment. Multiple methods were able to attain performance of around 88% AUC (area under the ROC curve), much higher performance than previous general-purpose methods. We present new acoustic monitoring datasets, summarise the machine learning techniques proposed by challenge teams, conduct detailed performance evaluation, and discuss how such approaches to detection can be integrated into remote monitoring projects.

cs.SD↗

arXiv2016

Bird detection in audio: a survey and a challenge

Many biological monitoring projects rely on acoustic detection of birds. Despite increasingly large datasets, this detection is often manual or semi-automatic, requiring manual tuning/postprocessing. We review the state of the art in automatic bird sound detection, and identify a widespread need for tuning-free and species-agnostic approaches. We introduce new datasets and an IEEE research challenge to address this need, to make possible the development of fully automatic algorithms for bird sound detection.

cs.SD↗

arXiv2013

Model-based functional mixture discriminant analysis with hidden process regression for curve classification

In this paper, we study the modeling and the classification of functional data presenting regime changes over time. We propose a new model-based functional mixture discriminant analysis approach based on a specific hidden process regression model that governs the regime changes over time. Our approach is particularly adapted to handle the problem of complex-shaped classes of curves, where each class is potentially composed of several sub-classes, and to deal with the regime changes within each homogeneous sub-class. The proposed model explicitly integrates the heterogeneity of each class of curves via a mixture model formulation, and the regime changes within each sub-class through a hidden logistic process. Each class of complex-shaped curves is modeled by a finite number of homogeneous clusters, each of them being decomposed into several regimes. The model parameters of each class are learned by maximizing the observed-data log-likelihood by using a dedicated expectation-maximization (EM) algorithm. Comparisons are performed with alternative curve classification approaches, including functional linear discriminant analysis and functional mixture discriminant analysis with polynomial regression mixtures and spline regression mixtures. Results obtained on simulated data and real data show that the proposed approach outperforms the alternative approaches in terms of discrimination, and significantly improves the curves approximation.

stat.ME↗

arXiv2013

Mixture model-based functional discriminant analysis for curve classification

Statistical approaches for Functional Data Analysis concern the paradigm for which the individuals are functions or curves rather than finite dimensional vectors. In this paper, we particularly focus on the modeling and the classification of functional data which are temporal curves presenting regime changes over time. More specifically, we propose a new mixture model-based discriminant analysis approach for functional data using a specific hidden process regression model. Our approach is particularly adapted to both handle the problem of complex-shaped classes of curves, where each class is composed of several sub-classes, and to deal with the regime changes within each homogeneous sub-class. The model explicitly integrates the heterogeneity of each class of curves via a mixture model formulation, and the regime changes within each sub-class through a hidden logistic process. The approach allows therefore for fitting flexible curve-models to each class of complex-shaped curves presenting regime changes through an unsupervised learning scheme, to automatically summarize it into a finite number of homogeneous clusters, each of them is decomposed into several regimes. The model parameters are learned by maximizing the observed-data log-likelihood for each class by using a dedicated expectation-maximization (EM) algorithm. Comparisons on simulated data and real data with alternative approaches, including functional linear discriminant analysis and functional mixture discriminant analysis with polynomial regression mixtures and spline regression mixtures, show that the proposed approach provides better results regarding the discrimination results and significantly improves the curves approximation.

stat.ME↗

arXiv2013

Physeter catodon localization by sparse coding

This paper presents a spermwhale' localization architecture using jointly a bag-of-features (BoF) approach and machine learning framework. BoF methods are known, especially in computer vision, to produce from a collection of local features a global representation invariant to principal signal transformations. Our idea is to regress supervisely from these local features two rough estimates of the distance and azimuth thanks to some datasets where both acoustic events and ground-truth position are now available. Furthermore, these estimates can feed a particle filter system in order to obtain a precise spermwhale' position even in mono-hydrophone configuration. Anti-collision system and whale watching are considered applications of this work.

cs.LG↗

arXiv2012

Stochastic Low-Rank Kernel Learning for Regression

We present a novel approach to learn a kernel-based regression function. It is based on the useof conical combinations of data-based parameterized kernels and on a new stochastic convex optimization procedure of which we establish convergence guarantees. The overall learning procedure has the nice properties that a) the learned conical combination is automatically designed to perform the regression task at hand and b) the updates implicated by the optimization procedure are quite inexpensive. In order to shed light on the appositeness of our learning strategy, we present empirical results from experiments conducted on various benchmark datasets.

cs.LG↗