Context-dependent environmental sound monitoring using SOM coupled with LEGION
Context-dependent environmental sound monitoring using SOM coupled with LEGION
复制标题
使用 SOM 与 LEGION 结合进行上下文相关的环境声音监测
DOI：
10.1109/ijcnn.2010.5596977
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
D. Botteldooren
中科院分区：
文献类型：
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作者：
Damiano Oldoni;B. D. Coensel;M. Rademaker;B. Baets;D. Botteldooren
环境声测量网络越来越多地用于监测城市环境中的噪声污染。智能测量节点提供了对环境声音进行高级分析的机会，但仍然必须在成本和功能之间进行权衡。当使用分层架构时，具有有限计算能力的本地节点可以用于检测潜在感兴趣的声音事件，然后由更强大的节点进一步分析。本文提出了一种模仿人类的模型，用于检测罕见和显着的声音事件。从声音中提取编码频谱-时间不规则性的特征，并且使用自组织映射（SOM）来识别最可能属于单个声音对象的共现特征。广泛的训练允许将此映射调整到在麦克风位置听到的典型声音。局部兴奋全局抑制振荡器网络（LEGION）被用来分组的SOM单位，以构建不同的声音对象。
Environmental sound measurement networks are increasingly applied for monitoring noise pollution in an urban context. Intelligent measurement nodes offer the opportunity to perform advanced analysis of environmental sound, but tradeoffs between cost and functionality still have to be made. When using a tiered architecture, local nodes with limited computing capabilities can be used to detect sound events of potential interest, which are then further analyzed by more powerful nodes. This paper presents a human-mimicking model for detecting rare and conspicuous sound events. Features encoding spectro-temporal irregularities are extracted from the sound, and a Self-Organizing Map (SOM) is used to identify co-occurring features, which most likely belong to a single sound object. Extensive training allows this map to be tuned to the typical sounds that are heard at the microphone location. A Locally Excitatory Globally Inhibitory Oscillator Network (LEGION) is used to group units of the SOM in order to construct distinct sound objects.