| Popis: |
Songbirds have been actively studied for their complex brain mechanism of sensor-motor integration during song learning. Male Bengalese finches learn singing by imitating external models to produce songs. In general, birdsong which is string of sounds is represented by a sequence of letters called song notes. In this study, we focus on information-theoretic analysis of these sequential data to explore the complexity and diversity of birdsong, and learning process throughout song development. We design and develop the analysis tool which has many features to do analysis for the sequential data. For experiment, we employ thirteen male Bengalese finches, each with different bouts of song data. By applying ethological data mining to these data, we discover that the finches follow two types of song learning mechanism: practice mode and adopt mode. In addition, over the analysis we find that it is possible to visualize the song features, e.g., traditional transmission, by contour surface diagram of the transition matrix. Furthermore, we can easily identify the families from these contour surface diagrams, which is a very challenging task in general. Our obtained results indicate that analysis based on data mining is a versatile technique to explore new aspects related to behavioral science. |
