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To bee or not to bee: Investigating machine learning approaches for
  beehive sound recognition
v1v2 (latest)

To bee or not to bee: Investigating machine learning approaches for beehive sound recognition

14 November 2018
I. Nolasco
Emmanouil Benetos
ArXiv (abs)PDFHTML

Papers citing "To bee or not to bee: Investigating machine learning approaches for beehive sound recognition"

5 / 5 papers shown
A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management
A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management
Willy Sucipto
Jianlong Zhou
Ray Seung Min Kwon
Fang Chen
148
0
0
10 Sep 2025
UrBAN: Urban Beehive Acoustics and PheNotyping Dataset
UrBAN: Urban Beehive Acoustics and PheNotyping Dataset
Mahsa Abdollahi
Yi Zhu
Heitor R. Guimarães
Nico Coallier
Ségolène Maucourt
Pierre Giovenazzo
Tiago H. Falk
314
6
0
05 Jun 2024
MSPB: a longitudinal multi-sensor dataset with phenotypic trait
  measurements from honey bees
MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees
Yi Zhu
Mahsa Abdollahi
Ségolène Maucourt
Nico Coallier
Heitor R. Guimarães
Pierre Giovenazzo
Tiago H. Falk
145
12
0
17 Nov 2023
Identify The Beehive Sound Using Deep Learning
Identify The Beehive Sound Using Deep LearningInternational Journal of Computer Science & Information Technology (IJCSIT) (IJCSIT), 2022
Shah Jafor Sadeek Quaderi
Sadia Afrin Labonno
Sadia Mostafa
S. Akhter
62
9
0
03 Sep 2022
Audio-based identification of beehive states
Audio-based identification of beehive statesIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018
I. Nolasco
A. Terenzi
S. Cecchi
S. Orcioni
Helen L. Bear
Emmanouil Benetos
178
89
0
15 Nov 2018
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