Applicability of VGGish embedding in bee colony monitoring: comparison with MFCC in colony sound classification
文献类型： 外文期刊
作者： Di, Nayan 1 ; Sharif, Muhammad Zahid 1 ; Hu, Zongwen 3 ; Xue, Renjie 1 ; Yu, Baizhong 1 ;
作者机构： 1.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Hefei Inst Phys Sci, Hefei, Peoples R China
2.Univ Sci & Technol China, Hefei, Peoples R China
3.Yunnan Agr Univ, Eastern Bee Res Inst, Coll Anim Sci & Technol, Kunming, Peoples R China
4.Yunnan Acad Agr Sci, Sericultural & Apicultural Res Inst, Mengzi, Peoples R China
关键词： Acoustic feature; Bee colony sound; VGGish embedding; Apis cerena; MFCC
期刊名称：PEERJ （ 影响因子：2.7； 五年影响因子：3.1 ）
ISSN： 2167-8359
年卷期： 2023 年 11 卷
页码：
收录情况： SCI
摘要： 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.
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