A Waveform Mapping-Based Approach for Enhancement of Trunk Borers’ Vibration Signals Using Deep Learning Model
The larvae of some trunk-boring beetles barely leave traces on the outside of trunks when feeding within, rendering the detection of them rather difficult. One approach to solving this problem involves the use of a probe to pick up boring vibrations inside the trunk and distinguish larvae activity a...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Insects |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4450/13/7/596 |
| _version_ | 1797406058943086592 |
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| author | Haopeng Shi Zhibo Chen Haiyan Zhang Juhu Li Xuanxin Liu Lili Ren Youqing Luo |
| author_facet | Haopeng Shi Zhibo Chen Haiyan Zhang Juhu Li Xuanxin Liu Lili Ren Youqing Luo |
| author_sort | Haopeng Shi |
| collection | DOAJ |
| description | The larvae of some trunk-boring beetles barely leave traces on the outside of trunks when feeding within, rendering the detection of them rather difficult. One approach to solving this problem involves the use of a probe to pick up boring vibrations inside the trunk and distinguish larvae activity according to the vibrations. Clean boring vibration signals without noise are critical for accurate judgement. Unfortunately, these environments are filled with natural or artificial noise. To address this issue, we constructed a boring vibration enhancement model named VibDenoiser, which makes a significant contribution to this rarely studied domain. This model is built using the technology of deep learning-based speech enhancement. It consists of convolutional encoder and decoder layers with skip connections, and two layers of SRU++ for sequence modeling. The dataset constructed for study is made up of boring vibrations of <i>Agrilus planipennis</i> Fairmaire, 1888 (Coleoptera: Buprestidae) and environmental noise. Our VibDenoiser achieves an improvement of 18.57 in SNR, and it runs in real-time on a laptop CPU. The accuracy of the four classification models increased by a large margin using vibration clips enhanced by our model. The results demonstrate the great enhancement performance of our model, and the contribution of our work to better boring vibration detection. |
| first_indexed | 2024-03-09T03:20:46Z |
| format | Article |
| id | doaj.art-bcc9a0ed49c944c184d139d99a7a0958 |
| institution | Directory Open Access Journal |
| issn | 2075-4450 |
| language | English |
| last_indexed | 2024-03-09T03:20:46Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Insects |
| spelling | doaj.art-bcc9a0ed49c944c184d139d99a7a09582023-12-03T15:11:40ZengMDPI AGInsects2075-44502022-06-0113759610.3390/insects13070596A Waveform Mapping-Based Approach for Enhancement of Trunk Borers’ Vibration Signals Using Deep Learning ModelHaopeng Shi0Zhibo Chen1Haiyan Zhang2Juhu Li3Xuanxin Liu4Lili Ren5Youqing Luo6School of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaBeijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, ChinaBeijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, ChinaThe larvae of some trunk-boring beetles barely leave traces on the outside of trunks when feeding within, rendering the detection of them rather difficult. One approach to solving this problem involves the use of a probe to pick up boring vibrations inside the trunk and distinguish larvae activity according to the vibrations. Clean boring vibration signals without noise are critical for accurate judgement. Unfortunately, these environments are filled with natural or artificial noise. To address this issue, we constructed a boring vibration enhancement model named VibDenoiser, which makes a significant contribution to this rarely studied domain. This model is built using the technology of deep learning-based speech enhancement. It consists of convolutional encoder and decoder layers with skip connections, and two layers of SRU++ for sequence modeling. The dataset constructed for study is made up of boring vibrations of <i>Agrilus planipennis</i> Fairmaire, 1888 (Coleoptera: Buprestidae) and environmental noise. Our VibDenoiser achieves an improvement of 18.57 in SNR, and it runs in real-time on a laptop CPU. The accuracy of the four classification models increased by a large margin using vibration clips enhanced by our model. The results demonstrate the great enhancement performance of our model, and the contribution of our work to better boring vibration detection.https://www.mdpi.com/2075-4450/13/7/596trunk-boring beetleboring vibrationdenoisingdeep learningend to endconvolutional recurrent neural network |
| spellingShingle | Haopeng Shi Zhibo Chen Haiyan Zhang Juhu Li Xuanxin Liu Lili Ren Youqing Luo A Waveform Mapping-Based Approach for Enhancement of Trunk Borers’ Vibration Signals Using Deep Learning Model Insects trunk-boring beetle boring vibration denoising deep learning end to end convolutional recurrent neural network |
| title | A Waveform Mapping-Based Approach for Enhancement of Trunk Borers’ Vibration Signals Using Deep Learning Model |
| title_full | A Waveform Mapping-Based Approach for Enhancement of Trunk Borers’ Vibration Signals Using Deep Learning Model |
| title_fullStr | A Waveform Mapping-Based Approach for Enhancement of Trunk Borers’ Vibration Signals Using Deep Learning Model |
| title_full_unstemmed | A Waveform Mapping-Based Approach for Enhancement of Trunk Borers’ Vibration Signals Using Deep Learning Model |
| title_short | A Waveform Mapping-Based Approach for Enhancement of Trunk Borers’ Vibration Signals Using Deep Learning Model |
| title_sort | waveform mapping based approach for enhancement of trunk borers vibration signals using deep learning model |
| topic | trunk-boring beetle boring vibration denoising deep learning end to end convolutional recurrent neural network |
| url | https://www.mdpi.com/2075-4450/13/7/596 |
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