Automatic Birdsong Splitting and Syllabic Analysis of Jalak Suren

Abstract

The study of birdsong has received relatively limited attention in the field of artificial intelligence, despite its long-standing intrigue and the question of whether birds possess a form of language. Previous research has provided evidence suggesting the presence of structurally organized words recognized by birds, such as the strong reactions observed in Japanese tits and Pied babblers when exposed to specific sequences of artificially played calls. Altering the speed of a sequence also influences the birds' responses, further supporting the existence of organized linguistic units in avian vocalizations. In this study, we propose a novel approach for analyzing birdsong by employing automatic syllable segmentation and syllabic similarity analysis. Our focus is on the Jalak Suren species (Sturnus contra), renowned for its melodious song. Through the identification and categorization of distinct syllabic units in birdsong recordings, we investigate the statistical occurrence of these syllables within the sequence of birdsong. Our findings reveal remarkable similarities between the statistical occurrence of syllables in birdsong and those found in human language passages