On the Studies of Syllable Segmentation and Improving MFCCs for Automatic Birdsong Recognition

Birdsongs are typically divided into four hierarchical levels: note, syllable, phrase, and song, of which syllable plays an important role in bird species recognition. To improve the recognition rate of birdsongs, in this study an enhanced syllable segmentation method based on R-S endpoint detection method was presented. Furthermore, a decision based neural network with suitable reinforcement learning rule was developed as the classifier. The proposed methods combined with the well-known MFCCs feature vector form a birdsong recognition system that was applied to two recognition problems: one is the recognition of a set of arbitrary syllables and the other is the recognition of a section of a birdsong. Experimental results show the performances of the proposed methods.