A sensor network for real-time acoustic scene analysis
A sensor network for real-time acoustic scene analysis
复制标题
用于实时声学场景分析的传感器网络
DOI：
10.1109/iscas.2009.5117712
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
A. Spanias
中科院分区：
文献类型：
--
作者：
H. Kwon;Harish Krishnamoorthi;Visar Berisha;A. Spanias
声学场景分析可以用于提取诸如国土安全、监视和环境监测等应用中的相关信息。无线传感器网络在监测声学场景方面特别感兴趣。嵌入在这样的网络中的传感器通常在诸如低功率和有限带宽的若干约束下操作。在本文中，我们考虑资源有效的声学传感任务，提取和传输相关信息的中心站，可以进行信息评估。我们提出了一系列的声学场景分析任务，在一个层次化的方式进行。分层任务包括声音和语音识别，从获得的声音，性别和情绪状态估计说话者的数量，最终语音监测和关键词定位。我们将支持向量机和高斯混合模型算法应用于声音特征。一个实时实现是使用十字弓微尘接口与TI DSP板。一系列的实验来表征在不同的条件下的算法的性能。
Acoustic scene analysis can be used to extract relevant information in applications such as homeland security, surveillance and environmental monitoring. Wireless sensor networks have been of particular interest in monitoring acoustic scenes. Sensors embedded in such a network typically operate under several constraints such as low power and limited bandwidth. In this paper, we consider resource-efficient acoustic sensing tasks that extract and transmit relevant information to a central station where information assessment can be conducted. We propose a series of acoustic scene analysis tasks that are performed in a hierarchical manner. Hierarchical tasks include sound and speech discrimination, estimation of the number of speakers from the acquired sound, gender and emotional state, and ultimately voice monitoring and key word spotting. We apply support vector machine and Gaussian mixture model algorithms on sound features. A real-time implementation is accomplished using Crossbow motes interfaced with a TI DSP board. A series of experiments are presented to characterize the performance of the algorithms under different conditions.