论文标题
图形：从部分连接的麦克风中提取的空间特征
Graph Cepstrum: Spatial Feature Extracted from Partially Connected Microphones
论文作者
论文摘要
在本文中，我们提出了一种使用部分同步和/或紧密位置的分布式麦克风进行声学场景分析的空间特征提取方法。在提出的方法中，利用基于图基的基础转换来从分布式麦克风中提取空间信息，同时考虑到是否引入了任何对麦克风对同步和/或紧密位置。具体而言，在拟议的基于图的cepstrum中，使用逆图傅立叶变换转换为多通道观察的对数振幅转换为特征向量，这是图形上信号的基础变换的一种方法。使用真实环境声音实验的结果表明，提出的基于图的Cepstrum鲁棒地提取空间信息，并考虑了麦克风连接。此外，结果表明，当观察到的声音在部分同步的麦克风组之间具有较大的同步不匹配时，所提出的方法比传统的空间特征更强大地对声学场景进行分类。
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.
