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

PeerJ

article

Nayan Di¹  Muhammad Zahid Sharif¹  Zongwen Hu³  Renjie Xue¹  Baizhong Yu¹ 

[1] Anhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences;University of Science and Technology of China;Eastern Bee Research Institute, College of Animal Science and Technology, Yunnan Agricultural University;The Sericultural and Apicultural Research Institute, Yunnan Academy of Agricultural Sciences

关键词: Acoustic feature;    Bee colony sound;    VGGish embedding;    Apis cerena;    MFCC;   

DOI  :  10.7717/peerj.14696

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

BackgroundBee 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.MethodsThis 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.ResultsThe results showed that VGGish embedding performs better than or on par with MFCC in all three datasets.

【 授权许可】

CC BY