Peso Parada Pablo, Cardenal-López Antonio
Multimedia Technologies Group, Department of Signal Processing and Communications, University of Vigo, 36310 Vigo, Spain.
J Acoust Soc Am. 2014 Jun;135(6):3371-80. doi: 10.1121/1.4876439.
In recent years, a number of automatic detection systems for free-ranging cetaceans have been proposed that aim to detect not just surfaced, but also submerged, individuals. These systems are typically based on pattern-recognition techniques applied to underwater acoustic recordings. Using a Gaussian mixture model, a classification system was developed that detects sounds in recordings and classifies them as one of four types: background noise, whistles, pulses, and combined whistles and pulses. The classifier was tested using a database of underwater recordings made off the Spanish coast during 2011. Using cepstral-coefficient-based parameterization, a sound detection rate of 87.5% was achieved for a 23.6% classification error rate. To improve these results, two parameters computed using the multiple signal classification algorithm and an unpredictability measure were included in the classifier. These parameters, which helped to classify the segments containing whistles, increased the detection rate to 90.3% and reduced the classification error rate to 18.1%. Finally, the potential of the multiple signal classification algorithm and unpredictability measure for estimating whistle contours and classifying cetacean species was also explored, with promising results.
近年来，已经提出了一些针对自由游动鲸类动物的自动检测系统，其目的不仅是检测浮出水面的个体，还包括检测潜入水中的个体。这些系统通常基于应用于水下声学记录的模式识别技术。利用高斯混合模型，开发了一种分类系统，该系统可以检测记录中的声音并将其分类为四种类型之一：背景噪声、啸叫声、脉冲声以及啸叫声和脉冲声的组合。使用2011年在西班牙海岸录制的水下记录数据库对该分类器进行了测试。采用基于倒谱系数的参数化方法，在分类错误率为23.6%的情况下，声音检测率达到了87.5%。为了改善这些结果，分类器中纳入了使用多重信号分类算法计算的两个参数和一个不可预测性度量。这些有助于对包含啸叫声的片段进行分类的参数，将检测率提高到了90.3%，并将分类错误率降低到了18.1%。最后，还探索了多重信号分类算法和不可预测性度量在估计啸叫轮廓和鲸类物种分类方面的潜力，结果很有前景。
