Narrow Results

Accurate identification of pig cough is essential for comprehensive monitoring and diagnosis of the respiratory health status of pigs. It contributes to stress-free animal health management, reduces pig mortality and improves the economics of farming. Creating a representative multisource signal signature of pig cough is a critical step in achieving automatic recognition of pig cough. For this reason, in this paper, we propose a feature fusion classification method that combines the spectrogram deep features and thermal image deep features to be fed into a support vector machine (SVM) classifier to accomplish cough classification. First, we use a time–frequency transformation algorithm to convert a one-dimensional cough sound signal into a two-dimensional acoustic spectrogram. Then, the corresponding heterogeneous deep features are extracted from the cough spectrogram and thermal image by fine-tuning Lenet-5 and a customized CoughRNet shallow convolutional neural network. Finally, we employ an early fusion technique to align and splice the extracted heterogeneous deep features and feed them into an SVM for the automatic classification task of pig cough. Our study evaluates the classification performance, recognition speed and model size of the proposed deep feature fusion classification network with satisfactory results. Experimental results show that the method achieves 99.77% accuracy in pig cough recognition. This further demonstrates the effectiveness of combining abstract heterogeneous sound and thermal image deep features as a method for automated detection of pig respiratory health.

Despite the great advances in the field of image classification, the association of ideal approaches that can bring improved results, considering different datasets, is still an open challenge. In this work, a novel approach is presented, based on a combination of compared strategies: feature extraction for early fusion; rankings based on manifold learning for late fusion; and feature augmentation applied in a long short-term memory (LSTM) algorithm. The proposed method aims to investigate the effect of feature fusion (early fusion) and ranking fusion (late fusion) in the final results of image classification. The experimental results showed that the proposed strategies improved the accuracy of results in different tested datasets (such as CIFAR10, Stanford Dogs, Linnaeus 5, Flowers 102, and Flowers 17) using a fusion of features from three convolutional neural networks (CNNs) (ResNet152, VGG16, and DPN92) and its respective generated rankings. The results indicated significant improvements and showed the potential of the approach proposed for image classification.