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Abstract 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.
Searfoss et al. (Mon,) studied this question.
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