Song Scope软件在通过声音自动识别动物物种中的应用
Abstract
Commercially available autonomous recorders for monitoring vocal wildlife populations such as birds and frogs now make it possible to collect thousands of hours of audio data in a field season. Given limited resources, it is not practical to manually review this volume of data “by ear”. The automatic processing of sound recordings to detect and identify specific species from their vocalizations, even if not perfectly accurate, makes efficient use of researchers who review only those samples most likely to contain vocalizations of interest. This results in significant gains of sample coverage, operating efficiency, and cost savings.
Developing generalized computer algorithms capable of accurate species identification in real-world field conditions is full of difficult challenges. First, recordings made by autonomous recorders typically receive sounds from all directions, scattered and reflected by trees, obscured by an unpredictable constellation of random noise, wind, rustling leaves, airplanes, road traffic, and other species of birds, frogs, insects and mammals. Second, the vocalizations of many species are highly varied from one individual to the next. Any algorithm must be prepared to accept vocalizations that are similar, but not identical, to known references in order to successfully detect the previously unobserved individual. However, in so doing, the algorithm is then susceptible to misclassifying a vocalization from a different species with similar components. This is especially true for species with narrowband vocalizations lacking distinctive spectral properties and in species with short duration vocalizations lacking distinctive temporal properties.
The bulk of prior research has generally differentiated among only a handful of simple mono-syllabic vocalizations at a time. While the results have been promising, we found that many approaches degrade significantly as the number of species increases, especially when more complex multi-syllabic and highly variable vocalizations are also included.
In this paper, we discuss an algorithm based on Hidden Markov Models automatically constructed so as to consider not just the spectral and temporal features of individual syllables, but also how syllables are organized into more complex songs. Additionally, several techniques are employed to reduce the effects of noise present in recordings made by autonomous recorders.
摘要：
用于监测鸟类和青蛙等有声野生动物种群的商用自动记录仪现在可以在野外季节收集数千小时的音频数据。鉴于资源有限，“凭耳朵”手动审查如此大量的数据是不切实际的。自动处理录音以从特定物种的叫声中检测和识别它们，即使不是完全准确，也能有效地利用只审查最有可能包含感兴趣叫声的样本的研究人员。这大大提高了样本覆盖率、运营效率和成本节约。
开发能够在现实世界的野外条件下准确识别物种的通用计算机算法充满了艰巨的挑战。首先，自动录音机的录音通常会接收来自各个方向的声音，这些声音被树木散射和反射，被不可预测的随机噪声、风、沙沙作响的树叶、飞机、道路交通和其他鸟类、青蛙、昆虫和哺乳动物的星座所掩盖。其次，许多物种的叫声因个体而异。任何算法都必须准备好接受与已知参考相似但不完全相同的发音，以便成功检测到以前未观察到的个体。然而，在这样做的过程中，该算法很容易对来自具有相似成分的不同物种的发音进行错误分类。对于缺乏独特光谱特性的窄带发声物种和缺乏独特时间特性的短时发声物种来说尤其如此。
之前的大部分研究通常一次只区分了少数简单的单音节发音。虽然结果很有希望，但我们发现，随着物种数量的增加，许多方法会显著退化，特别是当还包括更复杂的多音节和高度可变的发音时。
本文讨论了一种基于隐马尔可夫模型的自动构建算法，该算法不仅考虑了单个音节的频谱和时间特征，还考虑了音节如何组织成更复杂的歌曲。此外，还采用了几种技术来减少自主录音机录制的录音中存在的噪声的影响。
关键词：Song Scope软件，声音采集软件，野生动物声音监测，鸟鸣监测记录
