期刊
COMPUTERS AND ELECTRONICS IN AGRICULTURE
卷 197, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2022.106994
关键词
Pig cough; Feature fusion; Time-frequency representations; Convolutional neural networks
资金
- National Natural Science Foundation of China [32172784, 31902210]
- National Key Research and Development Program of China [2019YFE0125600]
- University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province [UNPYSCT-2020092]
- China Agriculture Research System of MOF and MARA [CARS-36, CARS-35]
This paper proposes a feature fusion method combining acoustic and deep features for pig cough recognition. The combination of acoustic and visual features is used to identify pig cough sounds, achieving an accuracy of 97.35%.
The recognition of pig cough sound is a prerequisite for early warning of respiratory diseases in pig houses, which is essential for detecting animal welfare and predicting productivity. With respect to pig cough recognition, it is a highly crucial step to create representative pig sound characteristics. To this end, this paper proposed a feature fusion method by combining acoustic and deep features from audio segments. First, a set of acoustic features from different domains were extracted from sound signals, and recursive feature elimination based on random forest (RF-RFE) was adopted to conduct feature selection. Second, time-frequency representations (TFRs) involving constant-Q transform (CQT) and short-time Fourier transform (STFT) were employed to extract visual features from a fine-tuned convolutional neural network (CNN) model. Finally, the ensemble of the two kinds of features was fed into support vector machine (SVM) by early fusion to identify pig cough sounds. This work investigated the performance of the proposed acoustic and deep features fusion, which achieved 97.35% accuracy for pig cough recognition. The results provide further evidence for the effectiveness of combining acoustic and deep spectrum features as a robust feature representation for pig cough recognition.
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