Timing of dawn chorus in songbirds along an anthropization gradient @ ICCB

On the basis of a field survey conducted on a breeding bird community (37 species) in spring 2022 in France, we disentangle the relative influence of such factors of the timing of bird chorus both at the species and community levels. Human activities are thus not only driving temporal changes in different bird species but also promote a change in the temporal structure of the chorus at the whole community.

BioacAI: Understanding animal sounds with machine learning

Official website: https://bioacousticai.eu The biodiversity crisis is coming into focus. Yet, data for monitoring wild animal populations are still incomplete and uncertain. And there are still big gaps in our understanding of animal behaviour and interactions. Animals make sounds that convey so much information. How can we use this to help monitor and protect wildlife?… Continue reading BioacAI: Understanding animal sounds with machine learning

PhD offer: “Theory and implementation of multi-resolution neural networks”

The French national center for scientific research (CNRS) is hiring a PhD student as part of a three-year project on “Multi-Resolution Neural Networks” (MuReNN). MuReNN is supported by the French national funding agency (ANR), and hosted at the Laboratoire des Sciences du Numérique de Nantes (LS2N). A collaboration with the Austrian Academy of Sciences is… Continue reading PhD offer: “Theory and implementation of multi-resolution neural networks”

Réseau thématique “Capteurs en environnement” (RTCE)

Site officiel : https://www.reseau-capteurs.cnrs.fr/ Forum : https://rtce.forum.inrae.fr Le réseau a pour vocation de rassembler l’ensemble des acteurs œuvrant dans les disciplines dédiées à la mesure in natura, en environnement naturel ou semi-naturel, souvent fortement anthropisé. L’ensemble des systèmes ou écosystèmes sont considérés : atmosphère, biosphère, terre interne ou externe, lacs, océans, glaciers et calotte de glace,… Continue reading Réseau thématique “Capteurs en environnement” (RTCE)

MuReNN: Multi-Resolution Neural Networks

“Less is more”, once the foundational motto of minimalist art, is making its way into artificial intelligence. After a maximalist decade of larger computers training larger neural networks on larger datasets (2012-2022), a countertrend arises. What if human-level performance could be achieved with less computing, less memory, and less supervision? In deep learning, the research… Continue reading MuReNN: Multi-Resolution Neural Networks

Explainable audio classification of playing techniques with layerwise relevance propagation @ IEEE ICASSP

Deep convolutional networks (convnets) in the time-frequency domain can learn an accurate and fine-grained categorization of sounds. For example, in the context of music signal analysis, this categorization may correspond to a taxonomy of playing techniques: vibrato, tremolo, trill, and so forth. However, convnets lack an explicit connection with the neurophysiological underpinnings of musical timbre perception. In this article, we propose a data-driven approach to explain audio classification in terms of physical attributes in sound production. We borrow from current literature in “explainable AI” (XAI) to study the predictions of a convnet which achieves an almost perfect score on a challenging task: i.e., the classification of five comparable real-world playing techniques from 30 instruments spanning seven octaves. Mapping the signal into the carrier-modulation domain using scattering transform, we decompose the networks’ predictions over this domain with layer-wise relevance propagation. We find that regions highly-relevant to the predictions localized around the physical attributes with which the playing techniques are performed.

Écologie de la musique numérique

Un article en langue française dans le dernier recueil du Centre national de la musique (CNM). En voici le résumé : Il est temps de renoncer à l’utopie d’une musique intégralement disponible, pour tout le monde, partout, tout de suite. Au contraire, le flux audio musical est matérialisé dans ses objets, limité dans ses architectures… Continue reading Écologie de la musique numérique

Announcing Kymatio tutorial @ ISMIR

We will present a tutorial on Kymatio at the International Society for Music Information Retrieval (ISMIR) Conference, held in Milan on November 5-9, 2023. Kymatio: Deep Learning meets Wavelet Theory for Music Signal Processing Kymatio is a Python package for applications at the intersection of deep learning and wavelet scattering. Its v0.4 release provides an… Continue reading Announcing Kymatio tutorial @ ISMIR

PETREL: Platform for Environmental Tracking of Renewable Energy and wildLife

Malgré leur intérêt évident dans la transition énergétique, les infrastructures productrices d’énergies renouvelables marines ont un impact sur la faune locale qui reste difficile à quantifier. Dans cecontexte, le projet PETREL (Platform for Environmental Tracking of Renewable Energy and wildLife) vise à inventer une solution pérenne et éco-responsable au problème du suivi environnemental des installations… Continue reading PETREL: Platform for Environmental Tracking of Renewable Energy and wildLife