A Computational Learning Theoretic Approach to Birdsong Syntax Analysis

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We propose an efficient automata-based approach to extract behavioral units and rules from continuous sequential data of animal behavior. Introducing original extensions, we integrate two elemental methods-the Ngram model and the Angluin's machine learning algorithm into an ethological data mining framework. It allows us to obtain the simplest finite automaton representation of behavioral rule that accepts (or generates) the smallest set of possible behavioral patterns from sequential data of animal behavior. With this method, we demonstrate how the ethological data mining works using real birdsong data and performs experimental evaluations of this method using artificial birdsong data generated by a computer program. These results suggest that our ethological data mining effectively works even for noisy ethological data by appropriately setting the parameters. In addition, we demonstrate a case study using the Bengalese finch song, showing that our method successfully grasps the core structure of the singing behavior such as loops and branchings.