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

Searfoss, Abigail M.; Pino, James C.; Creanza, Nicole

doi:10.1101/807974

Cited by 7 publications

(15 citation statements)

References 34 publications

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“…For both WAV and MP3 files, the chosen song was then exported in WAV format sampled at 44,100 Hz. We measured song properties using Chipper, software we developed in Python for signal processing, noise reduction, and syllable segmentation of bird song (Searfoss et al 2020b). With our software, a Gaussian‐windowed spectrogram (based on code from Gardner and Magnasco 2006) is produced from the WAV file and binarized according to a default signal threshold.…”

Section: Methodsmentioning

confidence: 99%

Detecting diel patterns in the songs of Chipping Sparrows using citizen‐science data

Searfoss

Liu²,

Creanza³

2020

J. Field Ornithol.

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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.

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Section: Methodsmentioning

confidence: 99%

Detecting diel patterns in the songs of Chipping Sparrows using citizen‐science data

Searfoss

Liu²,

Creanza³

2020

J. Field Ornithol.

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“…We excluded any species with less than 100 recordings. We also eliminated any species that sing continuously, or that do not have a clear separation between song bouts to facilitate consistent song analysis using our acoustic analysis program, Chipper [34].…”

Section: Species Selection Criteriamentioning

confidence: 99%

Cultural signatures of punctuated environmental change: measuring the effects of population-wide stress on song distribution in dark-eyed juncos and song sparrows

Snyder

Sellers

Creanza

2022

Preprint

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Severe weather events can dramatically alter a species' evolutionary trajectory. Previous landmark studies on populations in transiently hostile environments have measured traits that are under direct selection during those events. However, phenotype shifts that are not inherently adaptive to the fluctuating environment may also occur. Stress, especially during development, can cause important phenotypic changes in individuals, including impaired learning. Thus, learned behaviors, such as birdsong, may exhibit unique evolutionary dynamics as a result of widespread environmental stress. We hypothesize that ecosystem-level stressors may cause population-level changes to birdsong as entire cohorts experience developmental stress, learn songs imperfectly, and become the song tutors for future generations. In 2016, an unprecedented drought affected western New York State, a hotspot for community-science-generated birdsong recordings, and presented a unique opportunity to test our hypothesis through a natural experiment. We analyzed publicly available community-science birdsong recordings of two species, Junco hyemalis and Melospiza melodia, recorded between 2006-2020 in the drought-affected region and two control regions. We found that population-level song features had changed in the species with more complex songs (song sparrow) in the drought area after 2016, but not in the control area or in the species with a simple song (dark-eyed junco), implying that stress-induced deficits may disproportionately affect song traits that are more difficult to learn. These results demonstrate that environmental events can drive population-level trait evolution due to disruption in learning, with potentially important implications for speciation.

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“…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, e.g., Praat, Audacity, Chipper (Paul Boersma and David Weenink, 2021; Audacity Team, 2019; Searfoss et al, 2020)). To annotate birdsong, researchers follow a two-step process (Thompson et al, 1994;Kershenbaum et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Automated annotation of birdsong with a neural network that segments spectrograms

Cohen

Nicholson

Sanchioni

et al. 2022

eLife

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Songbirds provide a powerful model system for studying sensory-motor learning. However, many analyses of birdsong require time-consuming, manual annotation of its elements, called syllables. Automated methods for annotation have been proposed, but these methods assume that audio can be cleanly segmented into syllables, or they require carefully tuning multiple statistical models. Here we present TweetyNet: a single neural network model that learns how to segment spectrograms of birdsong into annotated syllables. We show that TweetyNet mitigates limitations of methods that rely on segmented audio. We also show that TweetyNet performs well across multiple individuals from two species of songbirds, Bengalese finches and canaries. Lastly, we demonstrate that using TweetyNet we can accurately annotate very large datasets containing multiple days of song, and that these predicted annotations replicate key findings from behavioral studies. In addition, we provide open-source software to assist other researchers, and a large dataset of annotated canary song that can serve as a benchmark. We conclude that TweetyNet makes it possible to address a wide range of new questions about birdsong.

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

Cited by 7 publications

References 34 publications

Detecting diel patterns in the songs of Chipping Sparrows using citizen‐science data

Detecting diel patterns in the songs of Chipping Sparrows using citizen‐science data

Cultural signatures of punctuated environmental change: measuring the effects of population-wide stress on song distribution in dark-eyed juncos and song sparrows

Automated annotation of birdsong with a neural network that segments spectrograms

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