Abstract
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 chewing. The 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 episode. Then, LSTM networks were employed to train classifiers for sound episode category classification. The 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 compared. Comparison results demonstrated that the introduction of DWT improved the classifier performance effectively, and PCA reduced the computational overhead without degrading classifier performance. The overall accuracy and comprehensive F1-score of the PW_MFCC based LSTM classifier were 94.97% and 97.41%, respectively. The classifier established in this study provided a foundation for an automatic identification system for sick sheep with abnormal feeding and rumination behaviour pattern. Keywords: sheep behaviour, short-term feeding behaviour, acoustic analysis, Mel-frequency cepstral coefficients, long-short term memory networks DOI: 10.25165/j.ijabe.20211402.6081 Citation: Duan G H, Zhang S F, Lu M Z, Okinda C, Shen M X, Norton T. Short-term feeding behaviour sound classification method for sheep using LSTM networks. Int J Agric & Biol Eng, 2021; 14(2): 43–54.
Highlights
Healthy sheep has a stable daily rhythm of grass intake at pasture[1]
In summary, it is evident in previous research that the most relevant explanatory variables of intake estimation for ruminants can be extracted from measurements of short-term feeding behaviour, including ingestion bite, ingestion chew, and rumination chew
Note: The last sub-sound clip labelled by a star point was discarded, whose length was less than the specific value (4096 sample points in this study)
Summary
Healthy sheep has a stable daily rhythm of grass intake at pasture[1]. Given that abnormalities in this rhythm can indicate health disorder, accurate measure of a sheep’s daily grass intake can be utilized to assess animal’s healthy status. Previous studies have reported a good correlation between the grass intake of individual ruminant animals and different short-term feeding behaviours[2,3,4,5,6,7]. It was found that the best estimation of grass intake of cows can be achieved when grazing time and bite frequency were used as predictors[3]. Rumination chewing frequency (chews per minute during rumination) were suggested to be the most significant explanatory variable of feed intake of cows[4]. In summary, it is evident in previous research that the most relevant explanatory variables of intake estimation for ruminants can be extracted from measurements of short-term feeding behaviour, including ingestion bite, ingestion chew, and rumination chew
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