Research on recognition method of broiler overlapping sounds based on random forest and confidence interval

Abstract

In response to the problem of difficult recognition of overlapping sounds in previous broiler health monitoring studies, in this paper, the author proposed a recognition method of broiler overlapping sounds based on random forest and confidence interval. The author newly defined the confidence rate, confidence interval and recognition error, and combined Random-Forest-based classifier (abbr. RF classifier) to realize the recognition of overlapping sounds in a fixed-duration broiler sound signal. In the research phase, the PXI-1050 model audio collection system was used to collect a large number of broiler sound signals, whereby multiple datasets corresponding to different sound types and overlapping sounds were established, and corresponding multiple first confidence rates were calculated. First, by analyzing the value interval of the first confidence rate of four sound types, including the cough, the crow, the purr and the flapping wing, respectively, the common value interval of the single sound type was obtained as [0.8, 1]. Second, by analyzing the value interval of the first confidence rate of the six sound type combinations in the double overlapping sounds, the common value interval of the double overlapping sounds was obtained as [0.45, 0.6]. On this basis, the overlapping interval was determined as [0, 0.7), and the pure interval was determined as [0.7, 1]. Then, by analyzing the value interval of the first confidence rate of four sound type combinations in the triple overlapping sounds, the common value interval of the triple overlapping sounds was obtained as [0.35, 0.45]. Therefore, within the overlapping interval, [0,0.35) was determined as the value interval of quadruple overlapping sounds, [0.35,0.45) was determined as the value interval of triple overlapping sounds, and [0.45,0.7) was determined as the value interval of double overlapping sounds. Finally, general procedure steps of the broiler overlapping sound recognition method used in the test phase was given. Multiple test results show that, for the recognition of sound types and overlapping sounds in broiler sound signals, the average recognition accuracy was 100%. For the recognition of overlapping degrees, the average recognition accuracy was 97.22%. For the recognition of sound type combinations, the average recognition error was 2.27%. It fully proves the good generalization effect of the method proposed in this paper, as well as its high practicability and robustness in practical tests. It is an important supplement to the existing broiler health monitoring study. Meanwhile, it can be applied to the research on recognition and separation of overlapping sounds or signals in similar fields, thus it has a high reference value.