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On contour-based classification of dolphin whistles by type

机译：基于轮廓的海豚哨声按类型分类

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摘要

Classification of cetacean vocalizations may help marine biologists study their behavioral context in different environments yet automatic classification of vocalizations for their information content has not been adequately addressed in the literature. Since classifier performance has a strong dependence on the extent to which features cluster, we, in this paper, explore the effect of two feature sets on two classifiers and assess their performance and computational complexity. We choose two feature sets that are exemplary of very different methods: The first set consists of Tempo-Frequency Parameters (TFPs) that are hand-picked to describe the spectral whistle contours. The second feature set embodies spectral information measured with the Fourier Descriptors (FD) commonly used in image processing for contour representation. The computed feature vectors are fed into the K-nearest neighbor (KNN) and Support Vector Machine (SVM) classification algorithms. The KNN in its basic form is a simple classifier that works well if feature clusters have clear margins and SVM uses a data dependent margin chosen for optimal performance. We argue that KNN serves to accentuate the effect of the feature sets and the SVM acts as the scientific process control. Experimental results show best results with the combination of the TFP feature extractor and the SVM classifier, suggesting a future research direction of developing non-linear kernels for SVM.
机译：鲸类发声的分类可以帮助海洋生物学家研究他们在不同环境中的行为背景，但文献中尚未对发声的信息内容进行自动分类。由于分类器的性能很大程度上取决于特征聚类的程度，因此在本文中，我们探讨了两个特征集对两个分类器的影响，并评估了它们的性能和计算复杂性。我们选择两个特征集，这些特征集是非常不同的方法的示例：第一个特征集是由速度频率参数（TFP）组成的，这些参数是手工挑选的以描述频谱哨声轮廓。第二个特征集包含用傅立叶描述符（FD）测量的光谱信息，该傅立叶描述符通常在图像处理中用于轮廓表示。计算出的特征向量被输入到K最近邻（KNN）和支持向量机（SVM）分类算法中。 KNN的基本形式是一个简单的分类器，如果要素簇具有明显的空白并且SVM使用为提高性能而选择的依赖数据的空白，则该分类器会很好地工作。我们认为，KNN用来强调功能集的作用，而SVM则充当科学过程控制。实验结果表明，结合TFP特征提取器和SVM分类器可以取得最佳效果，为开发SVM非线性内核提供了未来的研究方向。

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  • 来源
    《Applied Acoustics》 |2014年第2期|274-279|共6页
  • 作者

    Mahdi Esfahanian; Hanqi Zhuang; Nurgun Erdol;

  • 作者单位

    Department of Electrical and Computer Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA;

    Department of Electrical and Computer Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA;

    Department of Electrical and Computer Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Support vector machines; Fourier descriptor; Non-linear kernels; Pattern recognition; Dolphin whistles;

    机译：支持向量机;傅立叶描述符;非线性内核;模式识别;海豚哨;

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