Santa Dharma University, Mrican, Yogyakarta 55002, Indonesia.
J Acoust Soc Am. 2010 Feb;127(2):874-83. doi: 10.1121/1.3273887.
This paper presents an advanced method to acoustically assess animal abundance. The framework combines supervised classification (song-type and individual identity recognition), unsupervised classification (individual identity clustering), and the mark-recapture model of abundance estimation. The underlying algorithm is based on clustering using hidden Markov models (HMMs) and Gaussian mixture models (GMMs) similar to methods used in the speech recognition community for tasks such as speaker identification and clustering. Initial experiments using a Norwegian ortolan bunting (Emberiza hortulana) data set show the feasibility and effectiveness of the approach. Individually distinct acoustic features have been observed in a wide range of animal species, and this combined with the widespread success of speaker identification and verification methods for human speech suggests that robust automatic identification of individuals from their vocalizations is attainable. Only a few studies, however, have yet attempted to use individual acoustic distinctiveness to directly assess population density and structure. The approach introduced here offers a direct mechanism for using individual vocal variability to create simpler and more accurate population assessment tools in vocally active species.
本文提出了一种先进的声学方法来评估动物的丰富度。该框架结合了监督分类（歌曲类型和个体身份识别）、无监督分类（个体身份聚类）以及丰富度估计的标记重捕模型。该算法的基础是基于隐马尔可夫模型（HMM）和高斯混合模型（GMM）的聚类，类似于语音识别领域中用于说话人识别和聚类等任务的方法。使用挪威鹌鹑（Emberiza hortulana）数据集进行的初步实验表明了该方法的可行性和有效性。在广泛的动物物种中观察到了个体独特的声学特征，这与人类语音的说话人识别和验证方法的广泛成功表明，从其发声中可靠地自动识别个体是可行的。然而，只有少数研究试图利用个体声学特征来直接评估种群密度和结构。本文介绍的方法提供了一种直接的机制，用于利用个体发声的可变性来为发声活跃的物种创建更简单、更准确的种群评估工具。
