Convolutional Recurrent Neural Networks for the Classification of Cetacean Bioacoustic Patterns-Reference-Cited by-同舟云学术

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Convolutional Recurrent Neural Networks for the Classification of Cetacean Bioacoustic Patterns

Published:2023-06-04 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Makropoulos Dimitris N.¹,Tsiami Antigoni¹,Prospathopoulos Aristides²,Kassis Dimitris²,Frantzis Alexandros³,Skarsoulis Emmanuel⁴,Piperakis George⁴,Maragos Petros¹

Affiliation:

1. National Technical University of Athens,Greece

2. Institute of Oceanography,Hellenic Centre for Marine Research (HCMR),Anavyssos,Greece

3. Pelagos Cetacean Research Institute,Vouliagmeni,Greece,16671

4. IACM,Foundation of Research and Technology - Hellas,Heraklion-Crete,Greece

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10094559/10094560/10096422.pdf?arnumber=10096422

Reference24 articles.

1. A Convolutional Neural Network for Automated Detection of Humpback Whale Song in a Diverse, Long-Term Passive Acoustic Dataset

2. Per-Channel Energy Normalization: Why and How

3. Deep Machine Learning Techniques for the Detection and Classification of Sperm Whale Bioacoustics

4. Speech Processing in Mobile Environments

5. Monitoring Greek Seas Using Passive Underwater Acoustics

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1. A hybrid CNN–RNN model for rainfall–runoff modeling in the Potteruvagu watershed of India;CLEAN – Soil, Air, Water;2024-06-17

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