Results 171 to 180 of about 8,674 (182)

Perch 2.0: The Bittern Lesson for Bioacoustics

arXiv.org
Perch is a performant pre-trained model for bioacoustics. It was trained in supervised fashion, providing both off-the-shelf classification scores for thousands of vocalizing species as well as strong embeddings for transfer learning. In this new release,
B. V. Merrienboer, Vincent Dumoulin, Jenny Hamer, Lauren Harrell, Andrea Burns, Tom Denton   +5 more
semanticscholar   +1 more source

Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics

arXiv.org
Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments. However, high annotation and compute costs limit the field's efficacy.
Ben Williams, B. V. Merrienboer, Vincent Dumoulin, Jenny Hamer, Eleni Triantafillou, A. Fleishman, Matthew McKown, Jill E. Munger, Aaron N. Rice, Ashlee Lillis, Clemency E. White, Catherine A. D. Hobbs, Tries B. Razak, Kate E. Jones, Tom Denton   +14 more
semanticscholar   +1 more source

Comparative bioacoustics: a roadmap for quantifying and comparing animal sounds across diverse taxa

Biological Reviews of The Cambridge Philosophical Society, 2021
Animals produce a wide array of sounds with highly variable acoustic structures. It is possible to understand the causes and consequences of this variation across taxa with phylogenetic comparative analyses. Acoustic and evolutionary analyses are rapidly
Karan J. Odom, M. Araya‐Salas, Janelle L. Morano, Russell A. Ligon, G. Leighton, Conor C. Taff, Anastasia H. Dalziell, Alexis C. Billings, Ryan R. Germain, Michael Pardo, Luciana Guimarães de Andrade, Daniela Hedwig, S. Keen, Yu Shiu, R. Charif, M. Webster, A. Rice   +16 more
semanticscholar   +1 more source

Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics Processing

IEEE International Conference on Acoustics, Speech, and Signal Processing
Self-supervised learning (SSL) foundation models have emerged as powerful, domain-agnostic, general-purpose feature extractors applicable to a wide range of tasks.
Eklavya Sarkar, Mathew Magimai-Doss
semanticscholar   +1 more source

Bioacoustics Monitoring of Wildlife using Artificial Intelligence: A Methodological Literature Review

International Conference on Networking and Network Applications, 2022
Artificial intelligence (AI) is a broad computing science that has attracted significant attention in the ecological sector because of its problem-solving, deciding, and pattern recognition capabilities.
Sandhya Sharma, Kazuhiko Sato, B. P. Gautam   +2 more
semanticscholar   +1 more source

Deep Learning for Marine Bioacoustics and Fish Classification Using Underwater Sounds

Canadian Conference on Electrical and Computer Engineering, 2022
The migration of species is an important factor in the analysis of ecological systems. Changes in migratory patterns of a species or a specific group in an ecosystem often follow changes in the environment - many animals are sensitive to small changes ...
Jean-François Laplante, M. Akhloufi, C. Gervaise   +2 more
semanticscholar   +1 more source

Towards Deep Active Learning in Avian Bioacoustics

IAL@PKDD/ECML
Passive acoustic monitoring (PAM) in avian bioacoustics enables cost-effective and extensive data collection with minimal disruption to natural habitats. Despite advancements in computational avian bioacoustics, deep learning models continue to encounter
Lukas Rauch, Denis Huseljic, Moritz Wirth, Jens Decke, Bernhard Sick, Christoph Scholz   +5 more
semanticscholar   +1 more source

Bioacoustics Signal Authentication for E-Medical Records Using Blockchain

2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS)
Many are wary of storing and processing data in the cloud because of the prevalence of hostile assaults on mobile and wireless communication networks, which raises serious privacy and security concerns.
S. Sowjanya, Thota Sai, Lalith Prasad, Induru Sunnihith Reddy, Pundru Chandra, Shaker Reddy, Cheruku Muralikrishna, Vikas Sharma   +7 more
semanticscholar   +1 more source

Applying machine learning to primate bioacoustics: Review and perspectives

American Journal of Primatology
This paper provides a comprehensive review of the use of computational bioacoustics as well as signal and speech processing techniques in the analysis of primate vocal communication.
Jules Cauzinille, Benoît Favre, Ricard Marxer, Arnaud Rey   +3 more
semanticscholar   +1 more source

Few-Shot Bioacoustics Event Detection Using Transductive Inference With Data Augmentation

IEEE Sensors Letters
Few-shot (FS) sound event detection (SED) is the process of identifying and recognizing specific sounds or events within an audio recording, specifically in the field of bioacoustics.
Farhad Banoori, Nouman Ijaz, Jinglun Shi, Khalid Khan, Xiongying Liu, Sadique Ahmad, A. J. Prakash, Paweł Pławiak, Mohamed Hammad   +8 more
semanticscholar   +1 more source