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Birdsong Detection at the Edge with Deep Learning
Simone DIsabato
, Giuseppe Canonaco
, Paul G. Flikkema
, Manuel Roveri
, Cesare Alippi
Informatics, Computing, and Cyber Systems, School of
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
18
Scopus citations
Overview
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Keyphrases
Deep Learning
100%
Birdsong Detection
100%
Detection Accuracy
75%
Computational Complexity
50%
Computational Memory
50%
Birdsong
50%
Urbanization
25%
Internet of Things Devices
25%
Internet Technology
25%
Climate Change Impacts
25%
Power Usage
25%
Bird Species
25%
Classification Accuracy
25%
Training Intensity
25%
Best Solution
25%
Detection Algorithm
25%
Memory Demands
25%
Transfer Learning
25%
Machine Learning
25%
Bird Populations
25%
Signal Processing
25%
Computational Speed
25%
Spatial Limitation
25%
Internet of Things
25%
Computational Power
25%
STM32
25%
Wildlife Ecology
25%
Bird Vocalization
25%
Vocalization Classification
25%
Signal Machine
25%
Deep Neural Network
25%
Improved Detection
25%
Implementation Feasibility
25%
Vocal Detection
25%
ARM Cortex-M4
25%
Embedded Devices
25%
Song Detection
25%
Animal Ecology
25%
Detection Classification
25%
Real World Internet
25%
Deep Learning Scheme
25%
Human Observation
25%
Computer Science
Detection Accuracy
100%
Deep Learning
100%
Computational Complexity
66%
Internet of Things
66%
Speed-up
33%
Machine Learning
33%
Learning Mechanism
33%
Internet of Things Device
33%
Detection Algorithm
33%
Transfer Learning
33%
Embedded Device
33%
Deep Neural Network
33%
Human Observation
33%
Engineering
Internet of Things
100%
Deep Learning
100%
Computational Complexity
66%
Limitations
33%
Tasks
33%
Detection Algorithm
33%
Climate Change
33%
Transfer Learning
33%
Deep Neural Network
33%
M7 Processor
33%
Learning Mechanism
33%
Embedded Device
33%
Bird Song
33%
Great Importance
33%
Psychology
Internet of Things
100%
Neural Network
33%
Learning Transfer
33%