Probability Enhanced Entropy (PEE) Novel Feature for Improved Bird Sound Classification
文献类型：期刊论文
| 作者 | Ramashini Murugaiya1,2; Pg Emeroylariffion Abas2; Liyanage Chandratilak De Silva2 |
| 刊名 | Machine Intelligence Research
 |
| 出版日期 | 2022 |
| 卷号 | 19期号:1页码:52-62 |
| 关键词 | Bird sounds classification Gammatone frequency cepstral coefficient (GTCC) probability enhanced entropy (PEE) support vector machine (SVM) |
| ISSN号 | 2731-538X |
| DOI | 10.1007/s11633-022-1318-3 |
| 英文摘要 | Identification of bird species from their sounds has become an important area in biodiversity-related research due to the relative ease of capturing bird sounds in the commonly challenging habitat. Audio features have a massive impact on the classification task since they are the fundamental elements used to differentiate classes. As such, the extraction of informative properties of the data is a crucial stage of any classification-based application. Therefore, it is vital to identify the most significant feature to represent the actual bird sounds. In this paper, we propose a novel feature that can advance classification accuracy with modified features, which are most suitable for classifying birds from its audio sounds. Modified Gammatone frequency cepstral coefficient (GTCC) features have been extracted with their frequency banks adjusted to suit bird sounds. The features are then used to train and test a support vector machine (SVM) classifier. It has been shown that the modified GTCC features are able to give 86% accuracy with twenty Bornean birds. Furthermore, in this paper, we are proposing a novel probability enhanced entropy (PEE) feature, which, when combined with the modified GTCC features, is able to improve accuracy further to 89.5%. These results are significant as the relatively low-resource intensive SVM with the proposed modified GTCC, and the proposed novel PEE feature can be implemented in a real-time system to assist researchers, scientists, conservationists, and even eco-tourists in identifying bird species in the dense forest. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55927]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | 1.Department of Computer Science and Informatics, Uva Wellassa University, Badulla 90000, Sri Lanka 2.Faculty of Integrated Technologies, Universiti Brunei Darussalam, Bandar Seri Begawan BE1410, Brunei Darussalam |
| 推荐引用方式 GB/T 7714 | Ramashini Murugaiya,Pg Emeroylariffion Abas,Liyanage Chandratilak De Silva. Probability Enhanced Entropy (PEE) Novel Feature for Improved Bird Sound Classification[J]. Machine Intelligence Research,2022,19(1):52-62. |
| APA | Ramashini Murugaiya,Pg Emeroylariffion Abas,&Liyanage Chandratilak De Silva.(2022).Probability Enhanced Entropy (PEE) Novel Feature for Improved Bird Sound Classification.Machine Intelligence Research,19(1),52-62. |
| MLA | Ramashini Murugaiya,et al."Probability Enhanced Entropy (PEE) Novel Feature for Improved Bird Sound Classification".Machine Intelligence Research 19.1(2022):52-62. |
入库方式： OAI收割
来源：自动化研究所
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