4 citations · 4 across the 7 of their papers we have counts for
8 papers
Effects of interpulse-interval variation on deep-learning classification of bat vocalizations
Welmoed R. Eversteijn, Burooj Ghani, A. Leonie Baier +1
Temporal context may aid automated bat-species classification, but the contribution of specific features remains unclear. We investigated whether variation in the interpulse interv…
ChiroEcho: extending automated bat vocalisation classification beyond the learned taxonomy
Burooj Ghani, Welmoed Eversteijn, Milan van Hirtum +4
Bats are key indicators of ecosystem health and are protected throughout Europe, making reliable population monitoring a conservation priority. Their cryptic nocturnal lifestyle ma…
Torus embeddings
Dan Stowell
Many data representations are vectors of continuous values. In particular, deep learning embeddings are data-driven representations, typically either unconstrained in Euclidean spa…
Clustering and novel class recognition: evaluating bioacoustic deep learning feature extractors
Vincent S. Kather, Burooj Ghani, Dan Stowell
In computational bioacoustics, deep learning models are composed of feature extractors and classifiers. The feature extractors generate vector representations of the input sound se…
InsectSet459: an open dataset of insect sounds for bioacoustic machine learning
Marius Faiß, Burooj Ghani, Dan Stowell
Automatic recognition of insect sound could help us understand changing biodiversity trends around the world -- but insect sounds are challenging to recognize even for deep learnin…
Generalization in birdsong classification: impact of transfer learning methods and dataset characteristics
Burooj Ghani, Vincent J. Kalkman, Bob Planqué +3
Animal sounds can be recognised automatically by machine learning, and this has an important role to play in biodiversity monitoring. Yet despite increasingly impressive capabiliti…
Auto deep learning for bioacoustic signals
Giulio Tosato, Abdelrahman Shehata, Joshua Janssen +3
This study investigates the potential of automated deep learning to enhance the accuracy and efficiency of multi-class classification of bird vocalizations, compared against tradit…
Efficient speech detection in environmental audio using acoustic recognition and knowledge distillation
Drew Priebe, Burooj Ghani, Dan Stowell
The ongoing biodiversity crisis, driven by factors such as land-use change and global warming, emphasizes the need for effective ecological monitoring methods. Acoustic monitoring…