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short-term feeding behaviour sound classification method for sheep using LSTM networks

International Journal of Agricultural and Biological Engineering 2021年第2期14卷 43-54页

作者： Guanghui Duan Shengfu Zhang Mingzhou Lu Cedric Okinda Mingxia Shen Tomas Norton College of Artificial Intelligence Nanjing Agricultural UniversityNanjing 210031China Computer College Qinghai Nationalities UniversityXining 810007China M3-BIORES-Measure Model&Manage BioresponsesKU LeuvenHeverlee/Leuven 3001Belgium

A deep learning approach using long-short term memory(LSTM)networks was implemented in this study to classify the sound of short-term feeding behaviour of sheep,including biting,chewing,bolus regurgitation,and rumination *** original acoustic signal was split into sound episodes using an endpoint detection method,where the thresholds of short-term energy and average zero-crossing rate were utilized.A discrete wavelet transform(DWT),Mel-frequency cepstral,and principal-component analysis(PCA)were integrated to extract the dimensionally reduced DWT based Mel-frequency cepstral coefficients(denoted by PW_MFCC)for each sound ***,LSTM networks were employed to train classifiers for sound episode category *** performances of the LSTM classifiers with original Mel-frequency cepstral coefficients(MFCC),DWT based MFCC(denoted by W_MFCC),and PW_MFCC as the input feature coefficients were *** results demonstrated that the introduction of DWT improved the classifier performance effectively,and PCA reduced the computational overhead without degrading classifier *** overall accuracy and comprehensive F1-score of the PW_MFCC based LSTM classifier were 94.97%and 97.41%,*** classifier established in this study provided a foundation for an automatic identification system for sick sheep with abnormal feeding and rumination behaviour pattern.

关键词： sheep behaviour short-term feeding behaviour acoustic analysis Mel-frequency cepstral coefficients long-short term memory networks

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