Automated identification of field-recorded songs of four British grasshoppers using bioacoustic signal recognition
Automated identification of field-recorded songs of four British grasshoppers using bioacoustic signal recognition
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DOI：
10.1079/ber2004306
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发表时间：
2004-08-01
影响因子：
1.9
通讯作者：
Ohya, E
中科院分区：
文献类型：
--
作者：
Chesmore, ED;Ohya, E
直翅目物种的识别通过他们的歌声被广泛用于野外工作，但需要专业知识。现在有可能开发基于计算机的系统来实现相同的任务，具有许多优点，包括连续长期无人值守操作和自动物种记录。这里描述的系统实现了不同物种之间的自动判别，利用一种新的时域信号编码技术和人工神经网络。该系统此前已被证明可以识别25种英国直翅目昆虫，准确率为99%，声音质量良好。2002年对英格兰北方四种蝗虫的野外录音进行了测试，结果表明，该系统不仅能够在一定的声学条件下正确识别目标物种，而且能够识别鸟类和人造声音等其他声音。识别精度为四个物种的典型的70-100%，获得现场录音与不同的声音强度和背景信号。
Recognition of Orthoptera species by means of their song is widely used in field work but requires expertise. It is now possible to develop computer-based systems to achieve the same task with a number of advantages including continuous long term unattended operation and automatic species logging. The system described here achieves automated discrimination between different species by utilizing a novel time domain signal coding technique and an artificial neural network. The system has previously been shown to recognize 25 species of British Orthoptera with 99% accuracy for good quality sounds. This paper tests the system on field recordings of four species of grasshopper in northern England in 2002 and shows that it is capable of not only correctly recognizing the target species under a range of acoustic conditions but also of recognizing other sounds such as birds and man-made sounds. Recognition accuracies for the four species of typically 70-100% are obtained for field recordings with varying sound intensities and background signals.