research-article

Automatic acoustic classification of feline sex

Authors:

Maksim Kukushkin,

Stavros NtalampirasAuthors Info & Claims

AM '21: Proceedings of the 16th International Audio Mostly Conference

Pages 156 - 160

https://doi.org/10.1145/3478384.3478385

Published: 15 October 2021 Publication History

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.

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https://doi.org/10.3390/electronics12153206
Turchet LLagrange MRottondi CFazekas GPeters NØstergaard JFont FBäckström TFischione C(2023)The Internet of Sounds: Convergent Trends, Insights, and Future DirectionsIEEE Internet of Things Journal10.1109/JIOT.2023.325360210:13(11264-11292)Online publication date: 1-Jul-2023
https://doi.org/10.1109/JIOT.2023.3253602
Pisanò DServetti A(2023)Audio-aware applications at the edge using in-browser WebAssembly and fingerprinting2023 4th International Symposium on the Internet of Sounds10.1109/IEEECONF59510.2023.10335388(1-9)Online publication date: 26-Oct-2023
https://doi.org/10.1109/IEEECONF59510.2023.10335388
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Automatic acoustic classification of feline sex

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AM '21: Proceedings of the 16th International Audio Mostly Conference

September 2021

283 pages

ISBN:9781450385695

DOI:10.1145/3478384

Copyright © 2021 ACM.

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Publication History

Published: 15 October 2021

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AM '21

AM '21: Audio Mostly 2021

September 1 - 3, 2021

virtual/Trento, Italy

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Overall Acceptance Rate 177 of 275 submissions, 64%

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Cited By

Zhou FHu SWan XLu ZWu J(2023)Risevi: A Disease Risk Prediction Model Based on Vision Transformer Applied to Nursing HomesElectronics10.3390/electronics1215320612:15(3206)Online publication date: 25-Jul-2023
https://doi.org/10.3390/electronics12153206
Turchet LLagrange MRottondi CFazekas GPeters NØstergaard JFont FBäckström TFischione C(2023)The Internet of Sounds: Convergent Trends, Insights, and Future DirectionsIEEE Internet of Things Journal10.1109/JIOT.2023.325360210:13(11264-11292)Online publication date: 1-Jul-2023
https://doi.org/10.1109/JIOT.2023.3253602
Pisanò DServetti A(2023)Audio-aware applications at the edge using in-browser WebAssembly and fingerprinting2023 4th International Symposium on the Internet of Sounds10.1109/IEEECONF59510.2023.10335388(1-9)Online publication date: 26-Oct-2023
https://doi.org/10.1109/IEEECONF59510.2023.10335388
Bandara DExantus KNavarro-Martinez CPatterson MByun A(2023)Identifying Distinguishing Acoustic Features in Felid Vocalizations Based on Call Type and Species ClassificationAcoustics Australia10.1007/s40857-023-00298-551:3(345-357)Online publication date: 10-Jun-2023
https://doi.org/10.1007/s40857-023-00298-5

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