Abstract
The objective of this study is to investigate the proper settings and features for achieving robust performance of birdsong recognition system based on GMM and HMM models. The features include MFCCs, energy, pitch, formant, voicing degree and aperiodicity. We design the state numbers of HMM system for each bird model by the experience of observing birdsong. There are three rules to measure the recognition result, recognition rate, hit rate and accuracy. We collected birdsong recordings on sale for training and test data. There are 28 species of bird. Approximately all recordings can be separated into 2 groups. One is single species birdsong and the other is mixed species birdsong recordings. We labeled the attributes of each part of the sounds as the answer to the recognition. In the part of the recognition of one species in one sound, the recognition rate promoted 12% from the GMM system to the improved HMM system at 84.38%. On the other hand, the recognition of mixed species birdsongs, the hit rate is at 78%.