Birdsong analysis : a look inside from information science 情報科学の観点からの鳥の歌解析
Birdsong analysis : a look inside from information science
Khan Md. Mahfuzus Salam
カン モハマド マハフズス サラム
Songbirds have been actively studied for their complex brain mechanism of sensor-motor integration during song learning. In general, birdsong which is string of sounds is represented by a sequence of letters called song notes. Our subject bird Bengalese finch (Lonchura stri-ata var. domestica) has been widely studied for its unique song features similar to human language. Male Bengalese finches learn singing by imitating external models to produce songs. For computational analysis the songs must be represented in songnote sequences. An automated approach for this purpose is highly desired since manual processing makes human annotation cumbersome, and human annotation is very heuristic and easily lacks objectivity. In our research, we propose a new approach for automatic detection and recognition of the songnote sequences via image processing. The proposed method is based on human recognition process to visually identify the patterns in a sonogram image. The songnotes of the Bengalese finch are dependent on the birds and similar pattern does not exist in two different birds. Considering this constraint, our experiments on real birdsong data of different Bengalese finch show high accuracy rates for automatic detection and recognition of the songnotes. These results indicate that the proposed approach is feasible and generalized for any Bengalese finch songs. Furthermore, in our 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.