{"title":"猫科动物性别的自动声学分类","authors":"Maksim Kukushkin, S. Ntalampiras","doi":"10.1145/3478384.3478385","DOIUrl":null,"url":null,"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.","PeriodicalId":173309,"journal":{"name":"Proceedings of the 16th International Audio Mostly Conference","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Automatic acoustic classification of feline sex\",\"authors\":\"Maksim Kukushkin, S. Ntalampiras\",\"doi\":\"10.1145/3478384.3478385\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":173309,\"journal\":{\"name\":\"Proceedings of the 16th International Audio Mostly Conference\",\"volume\":\"51 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 16th International Audio Mostly Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3478384.3478385\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 16th International Audio Mostly Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3478384.3478385","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.