Abigail M. Searfoss

Previous studies have revealed diel patterns in the songs of Chipping Sparrows (Spizella passerina ), with songs shorter in duration before dawn than after. However, the extent to which this phenomenon generalizes to the full geographic range of these sparrows is unclear, as is the question of whether citizen‐science data can be used to detect diel patterns in song. We analyzed all available songs of Chipping Sparrows from the Macaulay Library and xeno‐canto databases and compared the distributions of song features of recordings made at different times of day. We show that, across their entire geographic range in North and Central America, Chipping Sparrows sing shorter songs before sunrise (dawn song) than after sunrise (day song). Furthermore, we show that Chipping Sparrows shorten their songs by singing fewer syllables, not by singing faster: the number of syllables per song accounts for the observed difference in duration, not the syllable nor the intersyllable duration. Our results demonstrate that recordings from public repositories can be used to determine whether daily song patterns exist in species even in the absence of prior fieldwork, and we further propose that citizen‐science recordings can be used to inform cross‐species hypotheses and facilitate future studies to determine whether diel patterns in song are associated with differences in social behavior.

Audio recording devices have changed significantly over the last 50 years, making large datasets of recordings of natural sounds, such as birdsong, easier to obtain. This increase in digital recordings necessitates an increase in high‐throughput methods of analysis for researchers. Specifically, there is a need in the community for open‐source methods that are tailored to recordings of varying qualities and from multiple species collected in nature. We developed Chipper, a Python‐based software to semi‐automate both the segmentation of acoustic signals and the subsequent analysis of their frequencies and durations. For avian recordings, we provide widgets to best determine appropriate thresholds for noise and syllable similarity, which aid in calculating note measurements and determining song syntax. In addition, we generated a set of synthetic songs with various levels of background noise to test Chipper's accuracy, repeatability and reproducibility. Chipper provides an effective way to quickly generate quantitative, reproducible measures of birdsong. The cross‐platform graphical user interface allows the user to adjust parameters and visualize the resulting spectrogram and signal segmentation, providing a simplified method for analysing field recordings. Chipper streamlines the processing of audio recordings with multiple user‐friendly tools and is optimized for multiple species and varying recording qualities. Ultimately, Chipper supports the use of citizen‐science data and increases the feasibility of large‐scale multi‐species birdsong studies.

Audio recording devices have changed significantly over the last 50 years, making large datasets of recordings of natural sounds, such as birdsong, easier to obtain. This increase in digital recordings necessitates an increase in high-throughput methods of analysis for researchers. Specifically, there is a need in the community for open-source methods that are tailored to recordings of varying qualities and from multiple species collected in nature. We developed Chipper, a Python-based software to semi-automate both the segmentation of acoustic signals and the subsequent analysis of their frequencies and durations. For avian recordings, we provide widgets to best determine appropriate thresholds for noise and syllable similarity, which aid in calculating note measurements and determining syntax. In addition, we generated a set of synthetic songs with various levels of background noise to test Chipper’s accuracy, repeatability, and reproducibility. Chipper provides an effective way to quickly generate reproducible estimates of birdsong features. The cross-platform graphical user interface allows the user to adjust parameters and visualize the resulting spectrogram and signal segmentation, providing a simplified method for analyzing field recordings. Chipper streamlines the processing of audio recordings with multiple user-friendly tools and is optimized for multiple species and varying recording qualities. Ultimately, Chipper supports the use of citizen-science data and increases the feasibility of large-scale multi-species birdsong studies.