Detection of sunn pests using sound signal processing methods
Citations Over TimeTop 10% of 2016 papers
Abstract
Extensive consumption of cereals as food in different domestic cousins places great demand the detection of cereal pest and struggle against them. Sunn pests such as Eurygaster integriceps, Eurygaster austriaca, Aelia rostrata and Aelia acuminata are insects with similar seasonal behaviors and dominant threat to the cereal plantations of Turkey. In this work, a microphone which works in acoustic and ultrasonic sound levels with the ability of making recordings with high frequency rate is used. Following the recording of sunn pest sounds with laboratory and outdoor conditions, the sound feature vectors are obtained with the application of different methods such as Linear Predictive Cepstral Coefficients (LPCC), Line Spectral Frequencies (LSF) and Mel Frequency Cepstral Coefficients (MFCC). By analyzing different kNN models it is shown that the automatic detection of sunn pests is possible with sound processing and machine learning methods. The best results is achieved with the overall accuracy of 93.6% using the combination of MFCC and LSF methods.
Related Papers
- → Infant cry recognition based on feature extraction(2010)3 cited
- → Infant asphyxia detection using autoencoders trained on locally linear embedded-reduced Mel Frequency Cepstrum Coefficient (MFCC) features(2018)2 cited
- → Pitch-based cepstral features for gender classification in noisy environments(2013)1 cited
- A Robust Mel-frequency Cepstrum Coefficients(2008)
- Application of Biomimetic Technology to Feature Extraction from Acoustic Objects(2014)