Wild bird singing voice analysis is addressed as an important issue from various aspects such as ethology, bioacoustics, robot audition, machine learning and so on. In the ethological aspect, people listen carefully, write down the type, direction and time of wild birds, and based on this memo, the analysis is conducted manually, requiring concentration and thus time consuming. Since such an analysis method is manual, it is difficult to obtain a unified analysis result, and furthermore, there is a problem that it cannot be confirmed again afterwards.
In recent years, overseas, some projects collecting wild bird singing voice have been launched, and research on the type identification of wild birds applying machine learning has been conducted. However, since their recording is done with a single microphone, those researches do not consider position detection of wild birds important in behavior analysis.
We are addressing the position detection task by fusion of robot audition and machine learning. Using multiple microphone arrays, we are engaged in development of the technology that detects the position of wild birds by estimating the three-dimensional position of the sound source over a wide area outdoors, and or the technology that distinguishes the type of wild birds from singing voices by a method of deep learning even with small amount of teacher data.In collaboration with research groups of Nagoya University and Kyoto University, we are also conducting research to develop these technologies and analyze communication by wild bird songs.