6 papers · 1 filter
CoarseSoundNet: Building a reliable model for ecological soundscape analysis
Alexander Gebhard, Andreas Triantafyllopoulos, Dominik Arend +4
A soundscape is composed of three types of sound: biophony (sounds made by animals), geophony (natural abiotic sounds) and anthropophony (sounds made by humans). A key research que…
Computer Audition: From Task-Specific Machine Learning to Foundation Models
Andreas Triantafyllopoulos, Iosif Tsangko, Alexander Gebhard +3
Foundation models (FMs) are increasingly spearheading recent advances on a variety of tasks that fall under the purview of computer audition -- the use of machines to understand so…
ECOSoundSet: a finely annotated dataset for the automated acoustic identification of Orthoptera and Cicadidae in North, Central and temperate Western Europe
David Funosas, Elodie Massol, Yves Bas +23
Currently available tools for the automated acoustic recognition of European insects in natural soundscapes are limited in scope. Large and ecologically heterogeneous acoustic data…
Exploring Meta Information for Audio-based Zero-shot Bird Classification
Alexander Gebhard, Andreas Triantafyllopoulos, Teresa Bez +3
Advances in passive acoustic monitoring and machine learning have led to the procurement of vast datasets for computational bioacoustic research. Nevertheless, data scarcity is sti…
Audio-based Step-count Estimation for Running -- Windowing and Neural Network Baselines
Philipp Wagner, Andreas Triantafyllopoulos, Alexander Gebhard +1
In recent decades, running has become an increasingly popular pastime activity due to its accessibility, ease of practice, and anticipated health benefits. However, the risk of run…
An automatic analysis of ultrasound vocalisations for the prediction of interaction context in captive Egyptian fruit bats
Andreas Triantafyllopoulos, Alexander Gebhard, Manuel Milling +2
Prior work in computational bioacoustics has mostly focused on the detection of animal presence in a particular habitat. However, animal sounds contain much richer information than…