Feature Extraction Techniques Based on Human Auditory System

Jain, Sanya; Gupta, Divya

doi:10.1007/978-981-10-7245-1_65

Cited by 2 publications

(1 citation statement)

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“…SCF′ q (n′) = SCF q (n′)×w(n′) (2) where, n′ is the index of each sample in the q th frame, 0 ≤ n′ ≤ npf -1; and w(n′) is the Hamming window [32] .…”

Section: Framing and Windowingmentioning

confidence: 99%

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

Duan

Zhang

et al. 2021

International Journal of Agricultural and Biological Engineering

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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.

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“…SCF′ q (n′) = SCF q (n′)×w(n′) (2) where, n′ is the index of each sample in the q th frame, 0 ≤ n′ ≤ npf -1; and w(n′) is the Hamming window [32] .…”

Section: Framing and Windowingmentioning

confidence: 99%