Using image processing to detect and classify narrow-band cricket and frog calls
Using image processing to detect and classify narrow-band cricket and frog calls
复制标题
DOI：
10.1121/1.2355479
复制
发表时间：
2006-11-01
影响因子：
2.4
通讯作者：
Figueroa, Harold K.
中科院分区：
文献类型：
--
作者：
Brandes, T. Scott;Naskrecki, Piotr;Figueroa, Harold K.
描述了一种自动呼叫识别（ACR）过程，该过程在声谱图图像上使用图像处理技术来检测和分类在低地哥斯达黎加雨林中发现的夜晚声音的背景中记录的恒定频率的蟋蟀和青蛙呼叫。该过程涉及使用图像模糊过滤器沿着阈值过滤器来隔离可能的呼叫事件。这些事件的特征，特别是事件的中心频率、持续时间和带宽，连同所应用的模糊滤波器的类型一起沿着，与贝叶斯分类器一起使用以进行不同呼叫的识别。在研究地点记录的22种不同的声型（假定为物种特异性的叫声）中，其中17种以足够高的数量记录，以训练和测试分类器。该分类器对这17种声型的真阳性准确率接近100%，但也有很高的假阴性率（4种声型超过50%）。这个过程的非常高的真阳性准确性使其能够用于监测热带森林中的蟋蟀（和一些青蛙物种）。(c)2006年，美国声学学会。
An automatic call recognition (ACR) process is described that uses image processing techniques on spectrogram images to detect and classify constant-frequency cricket and frog calls recorded amidst a background of evening sounds found in a lowland Costa Rican rainforest. This process involves using image blur filters along with thresholding filters to isolate likely calling events. Features of these events, notably the event's central frequency, duration and bandwidth, along with the type of blur filter applied, are used with a Bayesian classifier to make identifications of the different calls. Of the 22 distinct sonotypes (calls presumed to be species-specific) recorded in the study site, 17 of them were recorded in high enough numbers to both train and test the classifier. The classifier approaches 100% true-positive accuracy for these 17 sonotypes, but also has a high false-negative rate (over 50% for 4 sonotypes). The very high true-positive accuracy of this process enables its use for monitoring singing crickets (and some frog species) in tropical forests. (c) 2006 Acoustical Society of America.