This paper presents a novel method for classifying the feline sex based on the respective vocalizations. Due to the size of the available dataset, we rely on tree-based classifiers which can efficiently learn classification rules in such poor data availability cases. More specifically, this work investigates the ability of random forests and boosting classifiers when trained with a wide range of acoustic features derived both from time and frequency domain. The considered classifiers are evaluated using standardized figures of merit including f1-score, recall, precision, and accuracy. The best-performing classifier was the CatBoost, while the obtained results are in line with the state-of-the-art accuracy levels in the field of animal sex classification.
Automatic acoustic classification of feline sex / M. Kukushkin, S. Ntalampiras - In: AM '21: Audio Mostly 2021[s.l] : ACM, 2021. - ISBN 9781450385695. - pp. 156-160 (( convegno Audio Mostly tenutosi a Trento nel 2021 [10.1145/3478384.3478385].
|Titolo:||Automatic acoustic classification of feline sex|
NTALAMPIRAS, STAVROS (Corresponding)
|Parole Chiave:||audio signal processing; tree-based classifiers; audio pattern recognition; bioacoustics; domestic animals; Internet of Audio Things|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||2021|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1145/3478384.3478385|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|