Animal acoustic recognition means recognize animal species, individual, gender or even sound type through calls. The non-invasive and low-cost advantages of sound recognition compared to traditional ringing monitoring in field study motivate ecologists and behavioral scientists and zoologist to study about the methods of automatic animal acoustic recognition. In recent twenty years, prominent progresses have made in the technologies of acoustic recognition and classification based on the improvement got in human speech recognition and speaker recognition. Bird songs are variable and complex, which usually consist of four levels: notes, syllables, phrases and songs. Syllables are commonly used as the basic recognition unit, wherever there is no coherence method in syllable segmentation. Syllable features extracion and classifier for recognition are significant steps for recognition accuracy. Mel frequency cepstral coefficience(Davis and Mermelstein 1980) is the most effective feature used in acoustic recognition. Randomforest, which is a fast calculator and high-efficiency classfier in machine learning areas, has never been used in acoustic recognition. According to pre-works in animal acoustic recognition field, this work mainly focus on there parts of job contents. The first part is to recognize 33 kinds of frog species using the MFCC and randomforest methods. The second part of the work is about engaging MFCC and the randomforest methods to recognize 154 kinds of bird species, and the third part is to examine if the syllable types have effect on recognition accuracy. The results show that: 1. The MFCCs features and randomForest Classifier are suitable for this research, the accuracy for bird species recognition is 85.12%, for frogs is 97.7%. 2.The difference between syllable types used to train and test will vary bird species recognition accuracies, and the more similar the syllable types used in train process and test process, the higher accuracy will be got.
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