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.
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