A Fusion Feature for the Oestrous Sow Sound Identification Based on Convolutional Neural Networks

Abstract Affected by expert experience and individual differences in sows, artificial identification of sows in estrus is prone to various problems leading to low efficiency. A reliable audio classifier helps to identify the sows for estrus. In this work we propose a novel approach for estrus sound recognition using a fusion of two representative features as inputs and Convolutional Neural Networks (CNN) as training model. We demonstrate that the fusion feature generalizes better on the CNN model with training data under a real farm environment and gives remarkably higher test performance. Our model is also compared with some works that are published recently and achieve state-of-the-art performance.