USVSEG: A robust method for segmentation of ultrasonic vocalizations in rodents

Rodents' ultrasonic vocalizations (USVs) provide useful information for assessing their social behaviors. Despite previous efforts in classifying subcategories of time-frequency patterns of USV syllables to study their functional relevance, methods for detecting vocal elements from continuously...

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Bibliographic Details
Published in:PloS one 2020-02, Vol.15 (2), p.e0228907-e0228907
Main Authors: Tachibana, Ryosuke O, Kanno, Kouta, Okabe, Shota, Kobayasi, Kohta I, Okanoya, Kazuo
Format: Article
Language:English
Subjects:
Acoustics
Analysis
Animals
Background noise
Behavior
Biology and Life Sciences
Computer and Information Sciences
Databases, Factual
Discrimination
Engineering and Technology
Female
Gerbillinae - physiology
Humanities
Laboratories
Life sciences
Male
Methods
Mice
Mice, Inbred BALB C
Mice, Inbred C57BL
Muridae - physiology
Noise
Novels
Performance tests
Physical Sciences
Rats
Rats, Inbred Lew
Research and Analysis Methods
Rodentia - physiology
Rodents
Segmentation
Signal processing
Signal Processing, Computer-Assisted
Signal-To-Noise Ratio
Social aspects
Social behavior
Social Sciences
Sound
Sound Spectrography - methods
Sound Spectrography - statistics & numerical data
Syllables
Time
Ultrasonic testing
Ultrasonics - methods
Ultrasonics - statistics & numerical data
Vocalization, Animal - physiology
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Description
Summary:Rodents' ultrasonic vocalizations (USVs) provide useful information for assessing their social behaviors. Despite previous efforts in classifying subcategories of time-frequency patterns of USV syllables to study their functional relevance, methods for detecting vocal elements from continuously recorded data have remained sub-optimal. Here, we propose a novel procedure for detecting USV segments in continuous sound data containing background noise recorded during the observation of social behavior. The proposed procedure utilizes a stable version of the sound spectrogram and additional signal processing for better separation of vocal signals by reducing the variation of the background noise. Our procedure also provides precise time tracking of spectral peaks within each syllable. We demonstrated that this procedure can be applied to a variety of USVs obtained from several rodent species. Performance tests showed this method had greater accuracy in detecting USV syllables than conventional detection methods.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0228907