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

Abstract

This paper presents a sound recognition method for white feather broilers using spectrogram features and a fusion classification model, with the goal of achieving accurate classification of white feather broilers sound signals and providing a reliable basis for monitoring their health. In the training part, after five steps of sound signal acquisition, pre-processing, feature extraction, feature optimization, and model training, a fusion classification model with strong reliability is constructed for practical application scenarios. In the testing part, the method is applied to a real farming scenario of white feather broilers, and the stability of the multi-classification models and the reliability of the fusion classification model are verified. The fusion classification model comprises Random Forest, K-nearest neighbor, and RBF-based SVM. Results from multiple tests showed that the highest classification accuracies achieved by the three multi-classification models were 100%, 86.67%, and 93.33%, respectively. The average prediction accuracy of the fusion classification model on multiple audio signals was 98.57%, the results effectively demonstrate the feasibility and practicality of the proposed method.