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].

Automatic acoustic classification of feline sex

S. Ntalampiras
2021

Abstract

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.
audio signal processing; tree-based classifiers; audio pattern recognition; bioacoustics; domestic animals; Internet of Audio Things
Settore INF/01 - Informatica
Book Part (author)
File in questo prodotto:
File Dimensione Formato  
3478384.3478385.pdf

accesso riservato

Tipologia: Publisher's version/PDF
Dimensione 1.01 MB
Formato Adobe PDF
1.01 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/877550
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact