Abstract
this paper presents a comparison study in automatically identifying bird species based on bird acoustic signals, using audio files from XENO-CANTO online database. The features including Mel-Frequency Cepstral Coefficients (MFCC), geo-related meta-features, and the integration are compared. The learning classifiers Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Ensemble Learning are examined. Our experimental results show that in the comparison study, ensemble learning using discriminant learner with the integration of MFCC features and geo meta-features obtains the best detection performance.
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