Feature Selection Using Maximum Feature Tree Embedded with Mutual Information and Coefficient of Variation for Bird Sound Classification

The classification of bird sounds is important in ecological monitoring. Although extracting features from multiple perspectives helps to fully describe the target information, it is urgent to deal with the enormous dimension of features and the curse of dimensionality. Thus, feature selection is necessary. This paper proposes a scoring feature method named MICV (Mutual Information and Coefficient of Variation), which uses the coefficient of variation and mutual information to evaluate each feature's contribution to classification. And then, a method named ERMFT (Eliminating Redundancy Based on Maximum Feature Tree) based on two neighborhoods to eliminate redundancy to optimize features is explored. These two methods are combined as the MICV-ERMFT method to select the optimal features. Experiments are conducted to compare eight different feature selection methods with two sounds datasets of bird and crane. Results show that the MICV-ERMFT method outperforms other feature selection methods in the accuracy of the classification and is less time-consuming.