Automatic segmentation and classification of mice ultrasonic vocalizations.
J Acoust Soc Am
; 152(1): 266, 2022 07.
Article
en En
| MEDLINE
| ID: mdl-35931540
ABSTRACT
This paper addresses the development of a system for classifying mouse ultrasonic vocalizations (USVs) present in audio recordings. The automatic labeling process for USVs is usually divided into two main steps:
USV segmentation followed by the matching classification. Three main contributions can be highlighted (i) a new segmentation algorithm, (ii) a new set of features, and (iii) the discrimination of a higher number of classes when compared to similar studies. The developed segmentation algorithm is based on spectral entropy analysis. This novel segmentation approach can detect USVs with 94% and 74% recall and precision, respectively. When compared to other methods/software, our segmentation algorithm achieves a higher recall. Regarding the classification phase, besides the traditional features from time, frequency, and time-frequency domains, a new set of contour-based features were extracted and used as inputs of shallow machine learning classification models. The contour-based features were obtained from the time-frequency ridge representation of USVs. The classification methods can differentiate among ten different syllable types with 81.1% accuracy and 80.5% weighted F1-score. The algorithms were developed and evaluated based on a large dataset, acquired on diverse social interaction conditions between the animals, to stimulate a varied vocal repertoire.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Ultrasonido
/
Vocalización Animal
Tipo de estudio:
Prognostic_studies
Límite:
Animals
Idioma:
En
Revista:
J Acoust Soc Am
Año:
2022
Tipo del documento:
Article
País de afiliación:
Portugal