Improved Power Normalized Cepstrum Coefficient Based on Wavelet Packet Decomposition for Trunk Borer Detection in Harsh Acoustic Environment
نویسندگان
چکیده
The sound-detection method of trunk borer is a very promising in the field forestry prevention and control borers. However, detection accuracy commonly used algorithms often decreases sharply case noise reverberation interference. In practical applications, sound monitoring borers takes place harsh acoustic environment. To solve this problem, we intend to introduce methods which are effective other related fields. Unfortunately, most not suitable for perform extremely poorly. After trying various methods, found that Power-Normalized Cepstral Coefficients (PNCC) performed well some cases, while it did others. This due difference between speech sound. Therefore, an improved anti-noise PNCC based on wavelet package proposed. dmey wavlet system always obtains best performance. We collected audio following five dry pests testing. They red palm weevil, mountain pine beetle, necked longicorn, Asian longhorn beetle citrus beetle. experimental part, genetic algorithm-support vector machine (GA-SVM) as classifier compare Mel (MFCC), common borer, variety environments. results showed that, compared with newly proposed can achieve better results. above experiments take clips made clear pest mixed noise. order further verify effectiveness method, designed another experiment outdoor achieved 88% traditional 78% accuracy. cepstrum coefficient completely lost its ability distinguish. sum, packet decomposition be has many advantages, including simple extraction strong robustness Combined cheap acquisition equipment, effectively improve early warning pests.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2021
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app11052236