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.

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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

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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

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Section

Research Article - Bees