Parselmouth for bioacoustics: automated acoustic analysis in Python

Jadoul, Yannick; de Boer, Bart; Ravignani, Andrea

doi:10.1080/09524622.2023.2259327

Cited by 5 publications

(2 citation statements)

References 58 publications

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“…Praat is a popular software package originally developed to perform phonetic analysis and speech processing, but its wide range of acoustic algorithms and analyses have seen use in scientific fields beyond phonetics and linguistics. More specifically, Praat and Parselmouth have been used in a multitude of bioacoustics studies [8]. The algorithms implemented in Praat are particularly, though not exclusively, applicable to mammalian bioacoustics: Methods for tracking fundamental frequency, estimating formants, assessing voice quality, etc.…”

Section: Parselmouth In Bioacousticsmentioning

confidence: 99%

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Live-tracking Acoustic Parameters in Animal Behavioural Experiments: Interactive Bioacoustics with Parselmouth

Jadoul,

Duengen,

Ravignani

2024

Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023

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Section: Parselmouth In Bioacousticsmentioning

confidence: 99%

“…2. Once the space bar is released, Parselmouth takes the just-recorded audio fragment, filters out background noise with Praat's noise reduction algorithm 8 , and detects the seal's call by finding the longest non-silent interval 9 .…”

Section: Parselmouth In a Behavioural Experimentsmentioning

confidence: 99%

Live-tracking Acoustic Parameters in Animal Behavioural Experiments: Interactive Bioacoustics with Parselmouth

Jadoul,

Duengen,

Ravignani

2024

Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023

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Bioacoustic classification of a small dataset of mammalian vocalisations using deep learning

Manriquez P,

Kotz,

Ravignani

et al. 2024

Bioacoustics

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Training Experimentally Naive Seals for Vocal Learning Experiments

Duengen,

Ravignani

2024

Preprint

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Harbor seals (Phoca vitulina) are a common zoo species that show a scientifically valuable propensity for vocal learning. Under human care, the seals can be trained to associate vocalizations with cues. This ability is termed vocal usage learning and is characterized by learning to use a vocalization in a specific context. Among mammals, seals are prime candidates to investigate vocal learning. Yet, only a handful of reports exist on harbor seal vocal learning abilities and even less document how these were trained or tested. Here, we investigate how, and if, two experimentally naive harbor seals under human care, can be trained to participate in scientific experiments. We describe the training and testing of two seals in two basic vocal learning experiments. We trained the animals to vocalize upon the presentation of a discriminative stimuli (SD) through operant conditioning methods and tested their abilities to i) vocalize and refrain from vocalizing on two distinct SDs, and ii) produce two different vocalizations upon the presentation of two different SDs. Both seals learned the tasks: the first task was achieved within 118 trials (22 errors to criterion) and 220 trials (40 errors to criterion), the second task within 480 trials (158 errors to criterion) and 380 trials (94 errors to criterion), respectively. Our results confirm that harbor seals are capable of vocal usage learning and further suggest that associating individually distinct vocalizations with different SDs may be more cognitively demanding than vocalizing and being silent on SD.

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Parselmouth for bioacoustics: automated acoustic analysis in Python

Cited by 5 publications

References 58 publications

Live-tracking Acoustic Parameters in Animal Behavioural Experiments: Interactive Bioacoustics with Parselmouth

Live-tracking Acoustic Parameters in Animal Behavioural Experiments: Interactive Bioacoustics with Parselmouth

Bioacoustic classification of a small dataset of mammalian vocalisations using deep learning

Training Experimentally Naive Seals for Vocal Learning Experiments

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