Results 161 to 170 of about 33,488 (251)

Multi‐Platform Deployments of Low‐Cost Devices for Cetacean Passive Acoustic Monitoring

Remote Sensing in Ecology and Conservation, EarlyView.
Recent advances in affordable, user‐friendly devices offer new opportunities to overcome cost constraints of underwater passive acoustic monitoring (PAM) and expand acoustic data collection. In this study, we deployed low‐cost acoustic recorders and underwater cameras across a range of platforms in the Western Mediterranean, including fishing gear ...
Greta Jankauskaite, François Poisson, Irati Abascal, Javier Menéndez‐Blázquez, Clara Cánovas Pérez, Sofia Conti, Ricardo Sagarminaga, Juan Antonio Raga, David March   +8 more
wiley   +1 more source

A labeled RF signal dataset for UAV detection and classification in the 2.4GHz band under Wifi and Bluetooth coexistence. [PDF]

Data Brief
Mgannem S, Aloui R, Hamdi B, Mhatli S, Llamas-Garro I.   +4 more
europepmc   +1 more source

Passive Acoustic Identification of Social Groups in the Hainan Gibbon

Remote Sensing in Ecology and Conservation, EarlyView.
Passive acoustic monitoring offers a non‐invasive means of assessing visually hard‐to‐survey wildlife species with distinctive vocalizations. We evaluated whether deep learning can identify Hainan gibbon (Nomascus hainanus) social groups from their calls.
Emmanuel Kabuga, Emmanuel Dufourq, Nonhlanhla L. Luphade, Samuel T. Turvey, Heidi Ma, Susan M. Cheyne, Hui Liu, Jessica V. Bryant, Qing Chen, Wenyong Li, Zhiwei Liu, Zhaoli Zhou, Stefan Britz, Bubacarr Bah, Ian Durbach   +14 more
wiley   +1 more source

An empirical comparison of deep CNN architectures for heart sound classification from phonocardiogram signals. [PDF]

Front Physiol
Al Tawil A, Bin Sheeha B, Aburub A.
europepmc   +1 more source

Widespread Bomb Fishing in Indo‐Pacific Archipelago Revealed Through Machine Learning Accelerated Passive Acoustic Monitoring

Remote Sensing in Ecology and Conservation, EarlyView.
AI‐accelerated passive acoustic monitoring reveals extensive bomb fishing within the Spermonde Archipelago, with annual incidents numbering in the thousands. ABSTRACT Bomb fishing is recognised as the most destructive fishing practice that can be performed in our oceans.
Ben Williams, Mars Coral Restoration Project Monitoring Team, Aimee du Luart, Sonny Burniston, Tries B. Razak, Timothy A. C. Lamont, David J. Smith, Jamaluddin Jompa, David Curnick, Kate E. Jones   +9 more
wiley   +1 more source

Author Correction: A machine learning method to process voice samples for identification of Parkinson's disease. [PDF]

Sci Rep
Iyer A, Kemp A, Rahmatallah Y, Pillai L, Glover A, Prior F, Larson-Prior L, Virmani T.   +7 more
europepmc   +1 more source

Few Annotations, High Accuracy: Transfer Learning and Data Augmentation Improve Passive Acoustic Monitoring of the Vulnerable African Manatee

Remote Sensing in Ecology and Conservation, EarlyView.
Can deep learning reliably detect a threatened species in passive acoustic monitoring with few annotations? Across 52 sites and 600 recording days, we compared two deep learning recognizers with the traditional machine learning Kaleidoscope for detecting African manatee vocalizations.
Lucas Dubus, Auguste Verdier, Nina Giotto, Grace Mbemba, Gabriel Michelin, Baptiste Mulot, Stéphanie Manel, David Mouillot   +7 more
wiley   +1 more source

Multichannel high resolution NMF for modelling convolutive mixtures of non-stationary signals in the time-frequency domain [PDF]

, 2013
Badeau, Roland, Plumbley, Mark
core   +2 more sources

Repurposing Bird‐Based Acoustic Models for Amphibian Surveys: Strengths and Limitations in Real‐World Deployments

Remote Sensing in Ecology and Conservation, EarlyView.
In this study, we developed AnuraSearch, an open‐source classifier for 11 Ukrainian amphibian species, by repurposing the avian BirdNET algorithm via transfer learning to process over 18,000 h of autonomous acoustic recordings. Although the model demonstrated high precision and successfully reconstructed detailed seasonal and diel vocal activity ...
Vasylyna Strus, Yurii Strus, Nazar Smirnov, Anna Fedorova, Nataliia Suriadna, Mykola Drohvalenko   +5 more
wiley   +1 more source

Electrophysiological Indices of Hierarchical Speech Processing Differentially Reflect the Comprehension of Speech in Noise. [PDF]

eNeuro
Synigal SR, Anderson AJ, Lalor EC.
europepmc   +1 more source