Soundscape Indices: New Features for Classifying Beehive Audio Samples

Authors

  • Muhammad Zahid Sharif Chinese Academy of Sciences, Hefei, 230031, P.R. China; University of Science and Technology of China, Hefei, 230026, P.R. China. http://orcid.org/0000-0002-8980-8713
  • Fernando Wario University of Guadalajara Av. Juárez N° 976, Col. Centro, 44100, Guadalajara, Jalisco, México
  • Nayan Di Chinese Academy of Sciences, Hefei, 230031, P.R. China; University of Science and Technology of China, Hefei, 230026, P.R. China.
  • Renjie Xue Chinese Academy of Sciences, Hefei, 230031, P.R. China; University of Science and Technology of China, Hefei, 230026, P.R. China.
  • Fanglin Liu Institute of Technical Biology & Agriculture Engineering, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, P.R. China http://orcid.org/0000-0002-8371-6316

DOI:

https://doi.org/10.13102/sociobiology.v67i4.5860

Keywords:

Apis mellifera, bioacoustics, low-level signal features, machine learning, trichloromethane, soundscape index

Abstract

As the study of honey bee health has gained attention in the biology community, researchers have looked for new, non-invasive methods to monitor the health status of the colony. Since the beehive sound alters when the colony is exposed to stressors, analysis of the acoustic response of the colony has been used as a method to identify the type of stressor, whether it is chemical, pest, or disease. So far, two feature sets have been successfully used for this kind of analysis, being these low-level signal features and Mel Frequency Cepstral Coefficients (MFCC). Here we propose using soundscape indices, developed initially to delineate acoustic diversity in ecosystems, as an alternative to now used features. In our study, we examine the beehive acoustic response to trichloromethane laced-air and blank air and compare the performance of all three feature sets to discern the colony's sound between the hive being exposed to the chemical and not. Our results show that sound indices overperform the alternative features sets on this task. Based on these findings, we consider sound indices to be a valid set of features for beehive sound analysis and present our results to call the attention of the community on this fact.

Downloads

Download data is not yet available.

Author Biographies

Muhammad Zahid Sharif, Chinese Academy of Sciences, Hefei, 230031, P.R. China; University of Science and Technology of China, Hefei, 230026, P.R. China.

Ph.D. student at Institute of Technical Biology and Agriculture Engineering, Hefei Institutes of Physical Science,

Fernando Wario, University of Guadalajara Av. Juárez N° 976, Col. Centro, 44100, Guadalajara, Jalisco, México

Ph.D Scholar

Nayan Di, Chinese Academy of Sciences, Hefei, 230031, P.R. China; University of Science and Technology of China, Hefei, 230026, P.R. China.

Ph.D . student at Institute of Technical Biology and Agriculture Engineering, Hefei Institutes of Physical Science

Renjie Xue, Chinese Academy of Sciences, Hefei, 230031, P.R. China; University of Science and Technology of China, Hefei, 230026, P.R. China.

Ph.D . student at Institute of Technical Biology and Agriculture Engineering, Hefei Institutes of Physical Science

Fanglin Liu, Institute of Technical Biology & Agriculture Engineering, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, P.R. China

Professor at Hefei Institutes of Physical Science, Chinese Academy of Sciences

References

Breebaart, J., & McKinney, M. (2004). Features for audio classification. In Verhaegh W, Aarts E, Korst J (eds) Algorithms in Ambient Intelligence (pp. 113-129). Netherland: Kluwer Academic Publishers. DOI: https://doi.org/10.1007/978-94-017-0703-9_6

Breiman, L. (2001). Random Forests. Machine Learning 45: 5-32. DOI: https://doi.org/10.1023/A:1010933404324

Bromenshenk, J., Henderson, C., Seccomb, R., Welch, P., Debnam, S., & Firth, D. (2015). Bees as biosensors: chemosensory ability, honey bee monitoring systems, and emergent sensor technologies derived from the pollinator syndrome. Biosensors 5: 678-711. https://doi.10.3390/bios5040678 DOI: https://doi.org/10.3390/bios5040678

Bromenshenk, J.J., Henderson, C.B., Seccomb, R.A., Rice, S.D., & Etter, R.T. (2009). Honey bee acoustic recording and analysis system for monitoring hive health. U.S. Patent 7,549,907 B2

Bromenshenk, J.J., Seccomb, R.A., Rice, S.D., & Etter, R.T. (2005). Honey bee monitoring system for monitoring bee colonies in a hive. U.S. Patent 6,910,941 B2.

Calle, ML., & Urrea, V. (2011). Letter to the editor: Stability of Random Forest importance measures. Briefings in Bioinformatics 12: 86–9. https://doi.10.1093/bib/bbq011 DOI: https://doi.org/10.1093/bib/bbq011

Cecchi, S., Terenzi, A., Orcioni, S., & Piazza, F. (2019). Analysis of the sound emitted by honey bees in a beehive. In Audio Engineering Society Convention (p. 147). DOI: https://doi.org/10.1109/ISPA.2019.8868934

Cutler, D.R., Edwards, T.C, Beard, K.H, Cutler, A., Hess, K.T., Gibson, J., & Lawler, J.J. (2007). Random forests for classification in ecology. Ecology 88: 2783–2792. https://doi.org/10.1890/07-0539.1 DOI: https://doi.org/10.1890/07-0539.1

Dai, P.L., Qiang, W., & Hu-Sun, J. (2010). Effects of sublethal concentrations of bifenthrin and deltamethrin in fecundity, growth, and development of the honeybee Apis mellifera ligustica. Environmental Toxicology and Chemistry 29: 644-649. https://doi.10.1002/etc.67 DOI: https://doi.org/10.1002/etc.67

Ferrari, S., Silva, M., Guarino, & M., Berckmans, D. (2008). Monitoring of swarming sounds in bee hives for early detection of the swarming period. Computer in Electronics and Agriculture 64: 72-77.

https://doi.org/10.1016/j.compag.2008.05.010 DOI: https://doi.org/10.1016/j.compag.2008.05.010

Howard. D., Duran, O., Hunter, G., & Stebel, K. (2013). Signal processing the acoustics of honeybees (Apis Mellifera) to identify the 'Queenless' state in hives. Proceedings of the Institute of Acoustics 35: 290-297.

Li, D., Sethi, I.K., Dimitrova, N., & McGee, T. (2001). Classification of general audio data for content-based retrieval. Pattern Recognition Letters 22: 533-544. https://doi.org/10.1016/S0167-8655(00)00119-7 DOI: https://doi.org/10.1016/S0167-8655(00)00119-7

Liaw, A., & Wiener, M. (2002). Classification and regression by random forest. R News 2: 18-22.

Mammides, C., Goodale, E., Dayananda, S.K., Kang, L., & Chen, J. (2017). Do acoustic indices correlate with bird diversity? Insights from two biodiverse regions in Yunnan Province, south China. Ecological Indicators 82: 470-477. https://doi.org/10.1016/j.ecolind.2017.07.017 DOI: https://doi.org/10.1016/j.ecolind.2017.07.017

Meikle, W., & Holst, N. (2015). Application of continuous monitoring of honeybee colonies. Apidologie 46: 10-22. https://doi.10.1007/s13592-014-0298-x DOI: https://doi.org/10.1007/s13592-014-0298-x

Mezquida, D.A., & Martínez, J.L. (2009). Platform for beehives monitoring based on sound analysis. A perpetual warehouse for swarm's daily activity. Spanish Journal of Agriculture Research 7: 824-828. DOI: https://doi.org/10.5424/sjar/2009074-1109

Nolasco, I., & Benetos, E. (2018). To bee or not to bee: Investigating machine learning approaches for beehive sound recognition. arXiv 1811.06016.

Nolasco, I., Terenzi, A., Cecchi, S., Orcioni, S., Bear, H.L., & Benetos, E. (2019). Audio-based identification of beehive states. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 8256-8260). DOI: https://doi.org/10.1109/ICASSP.2019.8682981

McCauley, R. (2016). Long-term monitoring of soundscapes and deciphering a usable index: Examples of fish choruses from Australia. In Proceedings of Meetings on Acoustics (p. 010023). Dublin-Ireland.

Potts, S.G., Biesmeijer, J.C., Kremen, C., Neumann, P., Schweiger, O., Kunin, W.E. (2010). Global pollinator declines: trends, impacts and drivers. Trends in Ecology and Evolution. 25: 345-353. https://doi.org/10.1016/j.tree.2010.01.007 DOI: https://doi.org/10.1016/j.tree.2010.01.007

Qandour, A., Ahmad, I., Habibi, D., & Leppard, M. ( 2014). Remote beehive monitoring using acoustic signals. Acoustic Australia 42: 204-209.

Robles-Guerrero, A., Saucedo-Anaya, T., González-Ramérez, E., & Galván-Tejada, C.E. (2017). Frequency analysis of honey bee buzz for automatic recognition of health status: A preliminary study. Research in Comput Science. 142: 89-98. https://doi.10.13053/rcs-142-1-9 DOI: https://doi.org/10.13053/rcs-142-1-9

Rybin, V.G., Butusov, D.N., Karimov, T.I., Belkin, D.A., & Kozak, M.N. (2017). Embedded data acquisition system for beehive monitoring. In 2017 IEEE II International Conference on Control in Technical Systems (pp. 387-390). DOI: https://doi.org/10.1109/CTSYS.2017.8109576

Seccomb, R.A. (2014). Autonomous reporting and tracking of pesticide incidents in honey bee colonies. https://www.sbir.gov/printpdf/708862. (accessed date: 21 September, 2020).

Sharif, M.Z., Jiang, X., & Puswal, S.M. (2020). Pests, parasitoids, and predators: Can they degrade the sociality of a honeybee colony, and be assessed via acoustically monitored systems? Journal of Entomology and Zoology Studies 8: 1248-1260.

TeamRCore. (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Villanueva-Rivera, L., & Pijanowski, B. (2016). Soundecology: soundscape ecology. R package version 1.3. 2.

Yao, J., Zhu, Y.C., & Adamczyk, J. (2018). Responses of honey bees to lethal and sublethal doses of formulated clothianidin alone and mixtures. Journal of Economic Entomology 111: 1517-1525. https://doi.org/10.1093/jee/toy140. DOI: https://doi.org/10.1093/jee/toy140

Yang, Y., Ma, S., Liu, F., Wang, Q., Wang, X., Hou, C., Wu, Y., Gao, J., Zhang, L., Liu, Y., & Diao, Q. (2020). Acute and chronic toxicity of acetamiprid, carbaryl, cypermethrin and deltamethrin to Apis mellifera larvae reared in vitro. Pest Management Science. 76: 978-985. https://doi.10.1002/ps.5606. DOI: https://doi.org/10.1002/ps.5606

Downloads

Published

2020-12-28

How to Cite

Sharif, M. Z., Wario, F., Di, N., Xue, R., & Liu, F. (2020). Soundscape Indices: New Features for Classifying Beehive Audio Samples. Sociobiology, 67(4), 566–571. https://doi.org/10.13102/sociobiology.v67i4.5860

Issue

Vol. 67 No. 4 (2020)

Section

Research Article - Bees

License

Sociobiology is a diamond open access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access.

Authors who publish with this journal agree to the following terms:

 

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

 

    1. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

 

  1. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).