A novel chicken voice recognition method using the orthogonal matching pursuit algorithm

Cheng, Bingjie; Zhong, Shangping

doi:10.1109/cisp.2015.7408076

Cited by 5 publications

(3 citation statements)

References 19 publications

(19 reference statements)

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“…In addition, in the signal filtering method of this article, we choose the first method to determine the iterative stop condition of Sparse Decomposition. The author checked the relevant literature and found that Cheng et al [ 14 ] had also conducted the research on broiler signal filtering. In her research, when Sparse Decomposition is used for signal filtering, the second method is selected to determine the iterative stop condition, and the achieved signal filtering effect is excellent.…”

Section: Discussionmentioning

confidence: 99%

“…For example, in his research mentioned above, Carpentier, Lenn et al [ 10 ] used Spectral Subtraction to filter the sound signal before extracting features from broilers’ sneezes. Cheng et al [ 14 ] proposed a new broiler sound recognition method based on Sparse Representation. Specifically, it used Sparse Representation to reconstruct the sound signal, to complete signal filtering and feature extraction, and also used the SVM as the classification model to recognize the broiler sound in different environments.…”

Section: Introductionmentioning

confidence: 99%

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Research on Evaluating the Filtering Method for Broiler Sound Signal from Multiple Perspectives

Sun

Gao

Wang

et al. 2021

Animals

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Broiler sounds can provide feedback on their own body condition, to a certain extent. Aiming at the noise in the sound signals collected in broiler farms, research on evaluating the filtering methods for broiler sound signals from multiple perspectives is proposed, and the best performer can be obtained for broiler sound signal filtering. Multiple perspectives include the signal angle and the recognition angle, which are embodied in three indicators: signal-to-noise ratio (SNR), root mean square error (RMSE), and prediction accuracy. The signal filtering methods used in this study include Basic Spectral Subtraction, Improved Spectral Subtraction based on multi-taper spectrum estimation, Wiener filtering and Sparse Decomposition using both thirty atoms and fifty atoms. In analysis of the signal angle, Improved Spectral Subtraction based on multi-taper spectrum estimation achieved the highest average SNR of 5.5145 and achieved the smallest average RMSE of 0.0508. In analysis of the recognition angle, the kNN classifier and Random Forest classifier achieved the highest average prediction accuracy on the data set established from the sound signals filtered by Wiener filtering, which were 88.83% and 88.69%, respectively. These are significantly higher than those obtained by classifiers on data sets established from sound signals filtered by other methods. Further research shows that after removing the starting noise in the sound signal, Wiener filtering achieved the highest average SNR of 5.6108 and a new RMSE of 0.0551. Finally, in comprehensive analysis of both the signal angle and the recognition angle, this research determined that Wiener filtering is the best broiler sound signal filtering method. This research lays the foundation for follow-up research on extracting classification features from high-quality broiler sound signals to realize broiler health monitoring. At the same time, the research results can be popularized and applied to studies on the detection and processing of livestock and poultry sound signals, which has extremely important reference and practical value.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Evaluating the Filtering Method for Broiler Sound Signal from Multiple Perspectives

Sun

Gao

Wang

et al. 2021

Animals

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show abstract

“…For example, Lee et al [ 10 ] used the Support Vector Machine (SVM) as a classification model to identify the sound types while the laying hens suffered physical stress from changes in temperature and mental stress from fear. Cheng et al [ 11 ] proposed a new-type chicken voice recognition method. This method used sparse representation to complete the noise reduction and feature extraction from the reconstructed voices.…”

Section: Introductionmentioning

confidence: 99%

Recognition Method for Broiler Sound Signals Based on Multi-Domain Sound Features and Classification Model

Tao

Wang

Sun

et al. 2022

Sensors

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In view of the limited number of extracted sound features, the lack of in-depth analysis of applicable sound features, and the lack of in-depth study of the selection basis and optimization process of classification models in the existing broiler sound classification or recognition research, the author proposes a recognition method for broiler sound signals based on multi-domain sound features and classification models. The implementation process is divided into the training stage and the testing stage. In the training stage, the experimental area is built, and multiple segments of broiler sound signals are collected and filtered. Through sub-frame processing and endpoint detection, the combinations of start frames and end frames of multiple sound types in broiler sound signals are obtained. A total of sixty sound features from four aspects of time domain, frequency domain, Mel-Frequency Cepstral Coefficients (MFCC), and sparse representation are extracted from each frame signal to form multiple feature vectors. These feature vectors are labeled manually to build the data set. The min-max standardization method is used to process the data set, and the random forest is used to calculate the importance of sound features. Then, thirty sound features that contribute more to the classification effect of the classification model are retained. On this basis, the classification models based on seven classification algorithms are trained, the best-performing classification model based on k-Nearest Neighbor (kNN) is obtained, and its inherent parameters are optimized. Then, the optimal classification model is obtained. The test results show that the average classification accuracy achieved by the decision-tree-based classifier (abbreviated as DT classifier) on the data set before and after min–max standardization processing is improved by 0.6%, the average classification accuracy achieved by the DT classifier on the data set before and after feature selection is improved by 3.1%, the average classification accuracy achieved by the kNN-based classification model before and after parameter optimization is improved by 1.2%, and the highest classification accuracy is 94.16%. In the testing stage, for a segment of the broiler sound signal collected in the broiler captivity area, the combinations of the start frames and end frames of multiple sound types in the broiler sound signal are obtained through signal filtering, sub-frame processing, endpoint detection, and other steps. Thirty sound features are extracted from each frame signal to form the data set to be predicted. The optimal classification model is used to predict the labels of each piece of data in the data set to be predicted. By performing majority voting processing on the predicted labels of the data combination corresponding to each sound type, the common labels are obtained; that is, the predicted types are obtained. On this basis, the definition of recognition accuracy for broiler sound signals is proposed. The test results show that the classification accuracy achieved by the optimal classification model on the data set to be predicted is 93.57%, and the recognition accuracy achieved on the multiple segments of the broiler sound signals is 99.12%.

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Sound recognition method for white feather broilers based on spectrogram features and the fusion classification model

Lv,

Sun,

Zhang

et al. 2023

Measurement

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A novel chicken voice recognition method using the orthogonal matching pursuit algorithm

Cited by 5 publications

References 19 publications

Research on Evaluating the Filtering Method for Broiler Sound Signal from Multiple Perspectives

Research on Evaluating the Filtering Method for Broiler Sound Signal from Multiple Perspectives

Recognition Method for Broiler Sound Signals Based on Multi-Domain Sound Features and Classification Model

Sound recognition method for white feather broilers based on spectrogram features and the fusion classification model

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