Construction of sheep forage intake estimation models based on sound analysis

Sheng, Hang; Zhang, Shengfu; Zuo, Lishi; Duan, Guanghui; Zhang, Hailin; Okinda, Cedric; Shen, Mingxia; Chen, Kailing; Lu, Mingzhou; Norton, Tomás

doi:10.1016/j.biosystemseng.2020.01.024

Cited by 19 publications

(9 citation statements)

References 23 publications

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“…Existing strategies could be divided into two categories: binary classifier and multi-class classifier. The former focuses on distinguishing two kinds of feeding behaviour, such as chewing and non-chewing [25] , grazing and non-grazing [38] , grazing and ruminating [39] , and so on. The latter tries to distinguish multiple behaviours, such as biting, chewing, chew-bite, etc.…”

Section: Performance Comparison With Previous Methodsmentioning

confidence: 99%

“…Secondly, a double thresholds endpoint detection method, which was described in detail by Sheng et al [25] , was applied to split each enhanced audio section into sound and silence episodes. The former ones covered SE_ IB, SE_ IC, SE_ BR, SE_ RC, and SE_NSFB.…”

Section: Audio Section Segmentationmentioning

confidence: 99%

“…Features in audio signal produced by grazing or ingestion chew have been already utilized by many researchers to estimate forage or dry matter intake for sheep and cattle [5,12,25] . It was found that the times of ingestion bite can also contribute to intake estimation for sheep [2] .…”

Section: Potential Usage Of the Developed Classifiermentioning

confidence: 99%

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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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Section: Performance Comparison With Previous Methodsmentioning

confidence: 99%

Section: Audio Section Segmentationmentioning

confidence: 99%

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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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“…Similarly, Fontana et al [ 12 ] used peak frequency to classify broiler vocalizations. There have also been many attempts to analyze vocal data by converting them to a frequency domain such as a Fourier transform [ 13 , 14 , 15 ]. The Mel-frequency cepstral coefficient (MFCC) feature extraction technique includes windowing the signal, applying the discrete Fourier transform, taking the log of the magnitude, and warping the frequencies on a Mel scale.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering

Park

Kim

Moon

et al. 2021

Animals

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The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.

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“…In recent years, sound analysis as a non-invasive method has become an increasingly important tool in animal disease detection, behavior monitoring and welfare determination [ 4 , 5 , 6 ]. Cuan et al [ 7 ] proposed a sound recognition method based on convolutional neural network to detect the infection of avian influenza, yielding 90% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Sounds of Different Sources in a Commercial Broiler House

Yang

Zhao

et al. 2021

Animals

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Audio data collected in commercial broiler houses are mixed sounds of different sources that contain useful information regarding bird health condition, bird behavior, and equipment operation. However, characterizations of the sounds of different sources in commercial broiler houses have not been well established. The objective of this study was, therefore, to determine the frequency ranges of six common sounds, including bird vocalization, fan, feed system, heater, wing flapping, and dustbathing, at bird ages of week 1 to 8 in a commercial Ross 708 broiler house. In addition, the frequencies of flapping (in wing flapping events, flaps/s) and scratching (during dustbathing, scratches/s) behaviors were examined through sound analysis. A microphone was installed in the middle of broiler house at the height of 40 cm above the back of birds to record audio data at a sampling frequency of 44,100 Hz. A top-view camera was installed to continuously monitor bird activities. Total of 85 min audio data were manually labeled and fed to MATLAB for analysis. The audio data were decomposed using Maximum Overlap Discrete Wavelet Transform (MODWT). Decompositions of the six concerned sound sources were then transformed with the Fast Fourier Transform (FFT) method to generate the single-sided amplitude spectrums. By fitting the amplitude spectrum of each sound source into a Gaussian regression model, its frequency range was determined as the span of the three standard deviations (99% CI) away from the mean. The behavioral frequencies were determined by examining the spectrograms of wing flapping and dustbathing sounds. They were calculated by dividing the number of movements by the time duration of complete behavioral events. The frequency ranges of bird vocalization changed from 2481 ± 191–4409 ± 136 Hz to 1058 ± 123–2501 ± 88 Hz as birds grew. For the sound of fan, the frequency range increased from 129 ± 36–1141 ± 50 Hz to 454 ± 86–1449 ± 75 Hz over the flock. The sound frequencies of feed system, heater, wing flapping and dustbathing varied from 0 Hz to over 18,000 Hz. The behavioral frequencies of wing flapping were continuously decreased from week 3 (17 ± 4 flaps/s) to week 8 (10 ± 1 flaps/s). For dustbathing, the behavioral frequencies decreased from 16 ± 2 scratches/s in week 3 to 11 ± 1 scratches/s in week 6. In conclusion, characterizing sounds of different sound sources in commercial broiler houses provides useful information for further advanced acoustic analysis that may assist farm management in continuous monitoring of animal health and behavior. It should be noted that this study was conducted with one flock in a commercial house. The generalization of the results remains to be explored.

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Construction of sheep forage intake estimation models based on sound analysis

Cited by 19 publications

References 23 publications

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

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

Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering

Characterizing Sounds of Different Sources in a Commercial Broiler House

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