For a deeper understanding of ecological functions and semantics of wild bird vocalizations (i.e., songs and calls), it is important to clarify the fine-scaled and detailed relationships among their characteristics of vocalizations and their behavioral contexts. However, it takes a lot of time and effort to obtain such data using conventional recordings or by human observation. Bringing out a robot to a field is our approach to solve this problem. We are developing a portable observation system called HARKBird using a robot audition HARK and microphone arrays to understand temporal patterns of vocalizations characteristics and their behavioral contexts. In this paper, we introduce a prototype system to 2D localize vocalizations of wild birds in real-time, and to classify their song types after recording. We show that the system can estimate the position of songs of a target individual and classify their songs with a reasonable quality to discuss their song - behavior relationships.