Applicability of VGGish embedding in bee colony monitoring: comparison with MFCC in colony sound classification

Nayan Di; Muhammad Zahid Sharif; Zongwen Hu; Renjie Xue; Baizhong Yu

doi:10.7717/peerj.14696

Applicability of VGGish embedding in bee colony monitoring: comparison with MFCC in colony sound classification

PeerJ. 2023 Jan 26:11:e14696. doi: 10.7717/peerj.14696. eCollection 2023.

Authors

Nayan Di^{1

2}, Muhammad Zahid Sharif^{1

2}, Zongwen Hu^{3

4}, Renjie Xue^{1

2}, Baizhong Yu^{1

2}

Affiliations

¹ Anhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei, China.
² University of Science and Technology of China, Hefei, China.
³ Eastern Bee Research Institute, College of Animal Science and Technology, Yunnan Agricultural University, Kunming, China.
⁴ The Sericultural and Apicultural Research Institute, Yunnan Academy of Agricultural Sciences, Mengzi, China.

Abstract

Background: Bee colony sound is a continuous, low-frequency buzzing sound that varies with the environment or the colony's behavior and is considered meaningful. Bees use sounds to communicate within the hive, and bee colony sounds investigation can reveal helpful information about the circumstances in the colony. Therefore, one crucial step in analyzing bee colony sounds is to extract appropriate acoustic feature.

Methods: This article uses VGGish (a visual geometry group-like audio classification model) embedding and Mel-frequency Cepstral Coefficient (MFCC) generated from three bee colony sound datasets, to train four machine learning algorithms to determine which acoustic feature performs better in bee colony sound recognition.

Results: The results showed that VGGish embedding performs better than or on par with MFCC in all three datasets.

Keywords: Bee colony sound; Acoustic feature; Apis cerena; MFCC; VGGish embedding.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Acoustics*
Algorithms
Animals
Bees
Machine Learning
Recognition, Psychology
Sound*

Grants and funding

This work was supported by The Hefei Institutes of Physical Science, the Chinese Academy of Science. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.