Graph Cepstrum: Spatial Feature Extracted from Partially Connected Microphones
Graph Cepstrum: Spatial Feature Extracted from Partially Connected Microphones
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DOI：
10.1587/transinf.2019edp7162
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发表时间：
2020-01
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影响因子：
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通讯作者：
Keisuke Imoto
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文献类型：
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作者：
Keisuke Imoto
在本文中，我们提出了一种有效的和强大的空间特征提取方法的声学场景分析，利用部分同步和/或紧密分布的麦克风。在所提出的方法中，一个新的倒谱特征，利用基于图形的基础变换，从分布式麦克风提取空间信息，同时考虑到任何对麦克风是否同步和/或接近定位，被引入。具体地，在所提出的基于图的倒谱中，利用逆图傅立叶变换将多通道观测的对数幅度转换为特征向量，逆图傅立叶变换是一种在图上对信号进行基变换的方法。使用真实的环境声音的实验结果表明，所提出的基于图的倒谱鲁棒地提取空间信息与麦克风连接的考虑。此外，结果表明，所提出的方法更强大的分类声学场景比传统的空间特征时，所观察到的声音有一个大的同步失配部分同步麦克风组。
In this paper, we propose an effective and robust method of spatial feature extraction for acoustic scene analysis utilizing partially synchronized and/or closely located distributed microphones. In the proposed method, a new cepstrum feature utilizing a graph-based basis transformation to extract spatial information from distributed microphones, while taking into account whether any pairs of microphones are synchronized and/or closely located, is introduced. Specifically, in the proposed graph-based cepstrum, the log-amplitude of a multichannel observation is converted to a feature vector utilizing the inverse graph Fourier transform, which is a method of basis transformation of a signal on a graph. Results of experiments using real environmental sounds show that the proposed graph-based cepstrum robustly extracts spatial information with consideration of the microphone connections. Moreover, the results indicate that the proposed method more robustly classifies acoustic scenes than conventional spatial features when the observed sounds have a large synchronization mismatch between partially synchronized microphone groups.