Songbirds have been actively studied for their complex brain mechanism of sensor-motor integration during song learning. Our subject bird Bengalese finch has been widely studied for its unique song features similar to human language. For computational analysis the songs must be represented in songnote sequences. In chapter 3, we describe a new approach for automatic detection and recognition of the songnote sequences via image processing. Our experiments on real birdsong data of different Bengalese finch show high accuracy rates for automatic detection and recognition of the songnotes. Furthermore, in this book, we discuss on information-theoretic analysis of these sequential data to explore the complexity and diversity of birdsong, and learning process throughout song development. 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. Our obtained results indicate that analysis based on data mining is a versatile technique to explore new aspects related to behavioral science.