A Reversible Automata Approach to Modeling Birdsongs

Abstract

We propose a new automata-based approach to modeling birdsongs on the basis of Angluin's induction algorithm, which ensures that k-reversible languages can be learned from positive samples with polynomial time. There are similarities between Angluin's algorithm and the vocal learning of songbirds; for example, during a critical period, songbirds also learn songs from positive samples of conspecific birds. Using the proposed method, we demonstrate that the song syntaxes of the Bengalese finch can be represented as reversible automata with lower k-reversibility and that juvenile song syntaxes have two types of development. Our approach provides an effective way to understand the vocal learning of songbirds in terms of computational learning