Salomons Etto L, Havinga Paul J M
Ambient Intelligence Group, Saxion University of Applied Science, P.O. Box 70000, 7500KB Enschede, The Netherlands.
Pervasive Systems Group, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands.
Sensors (Basel). 2015 Mar 26;15(4):7462-98. doi: 10.3390/s150407462.
Wireless sensor networks are suitable to gain context awareness for indoor environments. As sound waves form a rich source of context information, equipping the nodes with microphones can be of great benefit. The algorithms to extract features from sound waves are often highly computationally intensive. This can be problematic as wireless nodes are usually restricted in resources. In order to be able to make a proper decision about which features to use, we survey how sound is used in the literature for global sound classification, age and gender classification, emotion recognition, person verification and identification and indoor and outdoor environmental sound classification. The results of the surveyed algorithms are compared with respect to accuracy and computational load. The accuracies are taken from the surveyed papers; the computational loads are determined by benchmarking the algorithms on an actual sensor node. We conclude that for indoor context awareness, the low-cost algorithms for feature extraction perform equally well as the more computationally-intensive variants. As the feature extraction still requires a large amount of processing time, we present four possible strategies to deal with this problem.
无线传感器网络适用于获取室内环境的情境感知。由于声波构成了丰富的情境信息源，为节点配备麦克风会大有裨益。从声波中提取特征的算法通常计算量极大。这可能会成为问题，因为无线节点通常资源受限。为了能够就使用哪些特征做出恰当决策，我们调研了文献中声音在全局声音分类、年龄和性别分类、情感识别、人员验证与识别以及室内外环境声音分类中的应用情况。将所调研算法的结果在准确性和计算量方面进行了比较。准确性取自所调研的论文；计算量通过在实际传感器节点上对算法进行基准测试来确定。我们得出结论，对于室内情境感知，低成本的特征提取算法与计算量更大的变体表现相当。由于特征提取仍需要大量处理时间，我们提出了四种可能的策略来处理这个问题。
