@article{abdollahiAudioModulationSpectral2025,

title = {Audio Modulation Spectral Features for Improved Honeybee Colony Population Prediction},

author = {M. Abdollahi and Y. Zhu and H. R. Guimaraes and N. Coallier and S. Maucourt and P. Giovenazzo and T. H. Falk},

url = {https://www.scopus.com/pages/publications/105020704883?origin=resultslist},

doi = {10.1109/JSEN.2025.3625178},

issn = {1530437X (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Sensors Journal},

volume = {25},

number = {24},

pages = {44378–44391},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Honeybees (Apis mellifera) are vital to agriculture and biodiversity, serving as primary pollinators for numerous crops and wild plants. However, the decline in bee populations due to factors such as pesticides, pathogens, parasites, and climate variations poses a serious threat to food security and ecological balance. This study introduces the use of modulation spectral features, extracted from beehive acoustic signals, to better predict the strength of the honeybee colony. Experiments conducted on the public urban beehive acoustics and phenotyping dataset (UrBAN), comprised of over 3000 h of beehive audio recordings, show the proposed features offering improved predictive performance compared with traditional audio features. This work underscores the potential of automated, noninvasive acoustic monitoring systems to support sustainable beekeeping and ecological preservation. © 2001-2012 IEEE.},

keywords = {Apis mellifera, Audio acoustics, Audio recordings, Beehive acoustic, Beehive acoustics, Biodiversity, Chemical contamination, Climate variation, Ecology, Food security, Food supply, Honeybee, Honeybee colonies, honeybees, Modulation spectrogram, Parasite-, Population statistics, Spectral feature, Spectrograms},

pubstate = {published},

tppubtype = {article}

}