Applicability of VGGish embedding in bee colony monitoring: comparison with MFCC in colony sound classification
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-01-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/14696.pdf |
| _version_ | 1797410440051949568 |
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| author | Nayan Di Muhammad Zahid Sharif Zongwen Hu Renjie Xue Baizhong Yu |
| author_facet | Nayan Di Muhammad Zahid Sharif Zongwen Hu Renjie Xue Baizhong Yu |
| author_sort | Nayan Di |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T04:30:03Z |
| format | Article |
| id | doaj.art-a9a96f43a50d442db93367d4f7b7de3d |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T04:30:03Z |
| publishDate | 2023-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-a9a96f43a50d442db93367d4f7b7de3d2023-12-03T13:37:23ZengPeerJ Inc.PeerJ2167-83592023-01-0111e1469610.7717/peerj.14696Applicability of VGGish embedding in bee colony monitoring: comparison with MFCC in colony sound classificationNayan Di0Muhammad Zahid Sharif1Zongwen Hu2Renjie Xue3Baizhong Yu4Anhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei, ChinaAnhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei, ChinaEastern Bee Research Institute, College of Animal Science and Technology, Yunnan Agricultural University, Kunming, ChinaAnhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei, ChinaAnhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei, ChinaBackground 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.https://peerj.com/articles/14696.pdfAcoustic feature Bee colony soundVGGish embeddingApis cerenaMFCC |
| spellingShingle | Nayan Di Muhammad Zahid Sharif Zongwen Hu Renjie Xue Baizhong Yu Applicability of VGGish embedding in bee colony monitoring: comparison with MFCC in colony sound classification PeerJ Acoustic feature Bee colony sound VGGish embedding Apis cerena MFCC |
| title | Applicability of VGGish embedding in bee colony monitoring: comparison with MFCC in colony sound classification |
| title_full | Applicability of VGGish embedding in bee colony monitoring: comparison with MFCC in colony sound classification |
| title_fullStr | Applicability of VGGish embedding in bee colony monitoring: comparison with MFCC in colony sound classification |
| title_full_unstemmed | Applicability of VGGish embedding in bee colony monitoring: comparison with MFCC in colony sound classification |
| title_short | Applicability of VGGish embedding in bee colony monitoring: comparison with MFCC in colony sound classification |
| title_sort | applicability of vggish embedding in bee colony monitoring comparison with mfcc in colony sound classification |
| topic | Acoustic feature Bee colony sound VGGish embedding Apis cerena MFCC |
| url | https://peerj.com/articles/14696.pdf |
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