Improved Power Normalized Cepstrum Coefficient Based on Wavelet Packet Decomposition for Trunk Borer Detection in Harsh Acoustic Environment

Abstract

The sound-detection method of trunk borer is a very promising method in the field of forestry prevention and control of trunk borers. However, the detection accuracy of commonly used algorithms often decreases sharply in the case of noise reverberation interference. In practical applications, the sound monitoring of trunk borers often takes place in a harsh acoustic environment. To solve this problem, we intend to introduce methods which are effective in other related acoustic fields. Unfortunately, most of the methods are not suitable for acoustic detection of trunk borers and perform extremely poorly. After trying various methods, we found that Power-Normalized Cepstral Coefficients (PNCC) performed well in some cases, while it did not in others. This is due to the difference between speech and trunk borer sound. Therefore, an improved anti-noise PNCC based on wavelet package is proposed. The dmey wavlet system always obtains the best performance. We collected the audio of the following five dry borer pests for testing. They are red palm weevil, mountain pine beetle, red necked longicorn, Asian longhorn beetle and citrus longhorn beetle. In the experimental part, we used genetic algorithm-support vector machine (GA-SVM) as a classifier to compare Mel Cepstral Coefficients (MFCC), which are the most common methods in the field of audio detection of trunk borer, PNCC and improved PNCC in a variety of noise environments. The results showed that, compared with other methods, the newly proposed method can often achieve better results. The above experiments take the audio clips made of clear pest sound mixed noise. In order to further verify the effectiveness of the method, we designed another experiment with a harsh outdoor acoustic environment. We found that the proposed method achieved 88% accuracy and the traditional PNCC achieved 78% accuracy. However, the Mel cepstrum coefficient completely lost its ability to distinguish. In sum, the proposed PNCC based on wavelet packet decomposition can be used as a detection method for trunk borer in the harsh acoustic environment. This method has many advantages, including simple extraction and strong robustness to noise. Combined with cheap audio acquisition equipment, this method can effectively improve the early warning ability of forestry borer pests.

Enhancing Red Palm Weevil Detection Using Bird Swarm Algorithm With Deep Learning Model

“…Visual analysis of the tree for the presence of signs, detection of the sound developed by nourishment larvae along chemical study of volatile signs produced by infected date palms are examined as the most recognized RPW earlier identification methods [4]. Moreover, an alternative standard technique of thermal imaging for monitoring temperature variations in diseased palms has to identify RPW.…”

“…Conventional methods and manual identification of insect pests have been inadequate, time-saving, and comparatively high-cost [9]. Consequently, earlier identification of the plant pests has a higher importance for agriculturalists to utilize appropriate pesticides, preventing crop loss [10]. Therefore, this analysis aims to develop an innovative solution for continuously monitoring the health of date palm trees against RPWs by implementing DL and IoT frameworks [11].…”

Enhancement of Boring Vibrations Based on Cascaded Dual-Domain Features Extraction for Insect Pest Agrilus planipennis Monitoring

“…When the SNR was −7 dB, the recognition accuracy decreased by 10.8% and 15.6% for their and the baseline model, respectively. Zhou et al [31] introduced improved anti-noise power normalized cepstral coefficients (PNCCs) based on the wavelet package for trunk borer vibrations. For a −5 dB SNR noise added to their data, the accuracy of their model decreased from 100% to 83%, with a further decline to 70% for −10 dB SNR.…”

A CNN-Based Method for Enhancing Boring Vibration with Time-Domain Convolution-Augmented Transformer

“…It is crucial to note that the quality of the collected signals is closely related to the detection accuracy of models. Zhou et al [15] proposed improved antinoise power-normalized cepstral coefficients (PNCCs) based on the wavelet package for detecting trunk borer vibrations. The detection accuracy was as high as 97% at 0 dB but decreased to 83% at −5 dB and further to 70% at −10 dB.…”