Research on filtering and classification method for white-feather broiler sound signals based on sparse representation

Abstract

Aiming at the problems in the existing white-feather broiler sound signal classification research, such as not considering the information and requirements in the real engineering application scenarios, not finding new applicable sound features, not providing the basis for the selection of classification learners, and the problems in the existing white-feather broiler sound signal filtering research, such as not carrying out in-depth research, not considering the signal characteristics, not analyzing the filtering effects in detail. Therefore, in this paper, the authors cleverly combined four methods of signal filtering, signal classification, machine learning, and sparse representation to propose a filtering and classification method for white-feather broiler sound signals based on sparse representation. Specifically, in terms of signal filtering, the signal characteristics of the white-feather broiler sound signal was carefully analyzed, a combination of noise reduction processing and signal reconstruction was newly proposed, and three classic and effective approximation algorithms were adopted, then the optimal filtering method of “using OMPA for noise reduction processing, and using 50-atom OMPA for signal reconstruction” was obtained. In terms of signal classification, in addition to the time-frequency domain and Mel-Frequency Cepstral Coefficients (abbr. MFCCs), time-frequency parameter features from sparse representations were newly extracted, and a total of 60-dimensional sound features were calculated on the frame signals, thus the data set was created. Feature engineering was performed on the data set, finally, the high-quality data set was obtained. Several common machine learning classification algorithms were used to train classification learners on the data set, and random forest with better performance was selected for parameter optimization. The parameter-optimized classification learner achieved the highest average prediction accuracy of 94.09%. Majority voting was newly proposed to process the prediction results to get the required signal classification result. On this basis, a new definition of classification accuracy suitable for white-feather broiler sound signals was newly proposed. Test results in real engineering application scenarios show that, the classification accuracy achieved by the proposed method on unknown white-feather broiler sound signals is about 88.89%. Compared with the existing research, the optimal filtering method considers the signal characteristics for the first time and achieves good filtering effects. The trained classification learner obtains excellent prediction effects and achieves good classification effects on several real sound types. This study is an important supplement to existing research on animal sound signal processing and classification, and provides important reference for the subsequent white-feather broiler health monitoring research.