Topic: nightingales

The rise of interactive AI in contextual information processing has enhanced tools for characterizing audio and speech. Behavioral neuroscientists and ecologists studying animal communication benefit greatly from these advancements. However, a universal solution for categorizing vocalizations is difficult due to audio variability as well as inter-species diversity. Our work aims to provide a more generalizable approach by developing a pipeline to segment field recordings from nightingales, which have a complex vocal repertoire of over 200 songs with 1000 different syllables. To identify bounda

Nightingales are renowned for their large song repertoire, including the acoustically distinct type of so-called whistle songs. These contain temporal sequences of unmodulated tonal syllables, known as whistle syllables, defining the fundamental frequency of the song, i.e., the pitch. While recent research has shown nightingales’ ability to adjust acoustic structure to match pitch, the flexibility of their temporal structure remains unexplored. Here, we show that nightingales exhibit a consistent rhythmic pattern in their whistle songs, with a shift in pitch distribution toward lower frequenci