Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Moummad, Ilyass"'
Acoustic identification of individual animals (AIID) is closely related to audio-based species classification but requires a finer level of detail to distinguish between individual animals within the same species. In this work, we frame AIID as a hie
Externí odkaz:
http://arxiv.org/abs/2409.08673
Passive acoustic monitoring (PAM) is crucial for bioacoustic research, enabling non-invasive species tracking and biodiversity monitoring. Citizen science platforms like Xeno-Canto provide large annotated datasets from focal recordings, where the tar
Externí odkaz:
http://arxiv.org/abs/2409.08589
Multi-label imbalanced classification poses a significant challenge in machine learning, particularly evident in bioacoustics where animal sounds often co-occur, and certain sounds are much less frequent than others. This paper focuses on the specifi
Externí odkaz:
http://arxiv.org/abs/2403.09598
Self-supervised learning (SSL) in audio holds significant potential across various domains, particularly in situations where abundant, unlabeled data is readily available at no cost. This is pertinent in bioacoustics, where biologists routinely colle
Externí odkaz:
http://arxiv.org/abs/2312.15824
Bioacoustic sound event detection allows for better understanding of animal behavior and for better monitoring biodiversity using audio. Deep learning systems can help achieve this goal, however it is difficult to acquire sufficient annotated data to
Externí odkaz:
http://arxiv.org/abs/2309.08971
Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning
Deep learning has been widely used recently for sound event detection and classification. Its success is linked to the availability of sufficiently large datasets, possibly with corresponding annotations when supervised learning is considered. In bio
Externí odkaz:
http://arxiv.org/abs/2309.00878
Autor:
Moummad, Ilyass, Farrugia, Nicolas
Methods based on supervised learning using annotations in an end-to-end fashion have been the state-of-the-art for classification problems. However, they may be limited in their generalization capability, especially in the low data regime. In this st
Externí odkaz:
http://arxiv.org/abs/2210.16192
Autor:
Moummad, Ilyass, Farrugia, Nicolas
Methods based on supervised learning using annotations in an end-to-end fashion have been the state-of-the-art for classification problems. However, they may be limited in their generalization capability, especially in the low data regime. In this st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fbe5324dff4f951ac900553817ea9bb2
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Moummad I; Medical Physics Department, CLCC François Baclesse, 14000 Caen, France., Jaudet C; Medical Physics Department, CLCC François Baclesse, 14000 Caen, France., Lechervy A; UMR GREYC, Normandie University, UNICAEN, ENSICAEN, CNRS, 14000 Caen, France., Valable S; ISTCT/CERVOxy Group, Normandie University, UNICAEN, CEA, CNRS, 14000 Caen, France., Raboutet C; Radiology Department, CLCC François Baclesse, 14000 Caen, France., Soilihi Z; Medical Physics Department, CLCC François Baclesse, 14000 Caen, France., Thariat J; Radiotherapy Department, CLCC François Baclesse, 14000 Caen, France., Falzone N; GenesisCare Theranostics, Building 1 & 11, The Mill, 41-43 Bourke Road, Alexandria, NSW 2015, Australia., Lacroix J; Radiology Department, CLCC François Baclesse, 14000 Caen, France., Batalla A; Medical Physics Department, CLCC François Baclesse, 14000 Caen, France., Corroyer-Dulmont A; Medical Physics Department, CLCC François Baclesse, 14000 Caen, France.; ISTCT/CERVOxy Group, Normandie University, UNICAEN, CEA, CNRS, 14000 Caen, France.
Publikováno v:
Cancers [Cancers (Basel)] 2021 Dec 22; Vol. 14 (1). Date of Electronic Publication: 2021 Dec 22.