Chipper: Open‐source software for semi‐automated segmentation and analysis of birdsong and other natural sounds

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.

Detecting cultural evolution in a songbird species using community-science data and computational modeling

“…In a previous study, we gathered and analyzed field-site and community-science recordings of chipping sparrows across the species’ entire breeding range ( Fig. S1 ), measuring numerous acoustic features of each song and classifying the syllables into distinct types and categories (Searfoss, Liu, et al, 2020; Searfoss, Pino, et al, 2020). A number of recorded songs in our previous analysis (Searfoss, Liu, et al, 2020) did not have a recording date listed; however, by revisiting the original field recording notes we were able to find the years for all recordings for our study presented here.…”

“…In addition, we used the song-analysis software Chipper to extract eight song features from each recording: mean intersyllable silence duration, mean syllable duration, mean syllable frequency range, mean syllable minimum and maximum frequency, duration of song bout, mean syllable stereotypy, and total number of syllables. Chipper allows the user to visualize each song bout, and it predicts where syllable boundaries are located using fluctuations in the amplitude of the signal (Searfoss, Pino, et al, 2020). The user can change the signal-to-noise threshold, apply lowpass and highpass filters to exclude high-frequency and low-frequency noise, respectively, and manually correct these syllable boundaries if necessary.…”

“…The user can change the signal-to-noise threshold, apply lowpass and highpass filters to exclude high-frequency and low-frequency noise, respectively, and manually correct these syllable boundaries if necessary. Then, Chipper analyzes the signal within each identified syllable and outputs a matrix of features for each song (see (Searfoss, Pino, et al, 2020) for more details on how each song feature is extracted from the spectrogram).…”

pykanto: A python library to accelerate research on wild bird song

“…In those cases, and depending on data volume, segmentation might better be performed either manually or in a semi‐automated way. For example, one could use chipper (Searfoss et al, 2020) or train a neural network like TweetyNet, (Cohen et al, 2022) on a manually annotated subset of the data.…”

Automated annotation of birdsong with a neural network that segments spectrograms

“…A major roadblock to scaling up many analyses is that they require researchers to annotate song. Annotation is a time-consuming process done by hand with graphical user interface (GUI) applications, for example Praat, Audacity, Chipper ( Boersma and Weenink, 2021 ; Audacity Team, 2019 ; Searfoss et al, 2020 ). To annotate birdsong, researchers follow a two-step process ( Thompson et al, 2012 ; Kershenbaum et al, 2016 ).…”