Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Beijing 100101, People's Republic of China.
J Acoust Soc Am. 2012 Apr;131(4):2859-65. doi: 10.1121/1.3692236.
Research into acoustic recognition systems for insects has focused on species identification rather than individual identification. In this paper, the feasibility of applying pattern recognition techniques to construct an acoustic system capable of automatic individual recognition for insects is investigated analytically and experimentally across two species of Orthoptera. Mel-frequency cepstral coefficients serve as the acoustic feature, and α-Gaussian mixture models were selected as the classification models. The performance of the proposed acoustic system is promising and displays high accuracy. The results suggest that the acoustic feature and classifier method developed here have potential for individual animal recognition and can be applied to other species of interest.
昆虫声学识别系统的研究主要集中在物种识别上，而不是个体识别上。本文从理论和实验两方面分析研究了将模式识别技术应用于构建能够对昆虫进行自动个体识别的声学系统的可行性。梅尔频率倒谱系数作为声学特征，选择α-高斯混合模型作为分类模型。所提出的声学系统的性能具有很大的潜力，显示出了很高的准确性。结果表明，这里开发的声学特征和分类器方法具有对个体动物识别的潜力，并可应用于其他感兴趣的物种。
