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

Abstract

Simple SummaryBroiler sound signals can reflect their own health status, such as crowing when they are hungry, coughing when they are in pain or ill, purring when there are foreign bodies in their throat, and flapping wings when they are fighting with each other. Many studies have been carried out to improve animal welfare based on this. However, the reality is that in addition to the emotional sounds mentioned above, there still exists back noise in the broiler sound signal collected in the farm, such as the noise of people walking and ventilation equipment. Therefore, it is necessary to adopt effective signal filtering methods to filter out these noises, and further obtain high-quality broiler sound signals for animal welfare research. In this study, five of the most classic and effective signal filtering methods were used to filter the back noise in broiler sound signals, and different evaluation indicators from two angles were used for “scoring” the filtering effect of each method in order to select the outstanding performers. These studies have laid the foundation for the follow-up study of broiler health monitoring.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.