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Improving Classification Algorithms by Considering Score Series in Wireless Acoustic Sensor Networks

机译：在无线声传感器网络中考虑分数序列改进分类算法

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摘要

The reduction in size, power consumption and price of many sensor devices has enabled the deployment of many sensor networks that can be used to monitor and control several aspects of various habitats. More specifically, the analysis of sounds has attracted a huge interest in urban and wildlife environments where the classification of the different signals has become a major issue. Various algorithms have been described for this purpose, a number of which frame the sound and classify these frames, while others take advantage of the sequential information embedded in a sound signal. In the paper, a new algorithm is proposed that, while maintaining the frame-classification advantages, adds a new phase that considers and classifies the score series derived after frame labelling. These score series are represented using cepstral coefficients and classified using standard machine-learning classifiers. The proposed algorithm has been applied to a dataset of anuran calls and its results compared to the performance obtained in previous experiments on sensor networks. The main outcome of our research is that the consideration of score series strongly outperforms other algorithms and attains outstanding performance despite the noisy background commonly encountered in this kind of application.
机译：许多传感器设备的尺寸，功耗和价格的下降使得许多传感器网络的部署成为可能，这些网络可用于监视和控制各种栖息地的各个方面。更具体地说，声音的分析在城市和野生环境中引起了极大的兴趣，在这些环境中，不同信号的分类已成为一个主要问题。为此已经描述了各种算法，其中许多算法对声音进行帧化并对这些帧进行分类，而其他算法则利用嵌入在声音信号中的顺序信息。在本文中，提出了一种新算法，该算法在保持帧分类优势的同时，增加了一个新阶段，该阶段考虑并分类在帧标记后得出的得分序列。这些分数系列使用倒频谱系数表示，并使用标准的机器学习分类器进行分类。所提出的算法已应用于无水牛呼叫的数据集，并将其结果与以前在传感器网络上的实验中获得的性能进行了比较。我们研究的主要结果是，尽管在这种应用程序中经常遇到嘈杂的背景，但分数序列的考虑却大大优于其他算法并获得了出色的性能。

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