Microphone Multiplexing with Diffuse Noise Model-based Principal Component Analysis
Microphone Multiplexing with Diffuse Noise Model-based Principal Component Analysis
复制标题
麦克风复用与基于扩散噪声模型的主成分分析
DOI：
10.1109/waspaa.2013.6701877
复制
发表时间：
2013
期刊：
影响因子：
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通讯作者：
Nobuataka Ono and Laurent Daudet
中科院分区：
文献类型：
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作者：
Sonia Badar;Nobuataka Ono and Laurent Daudet
减少麦克风阵列的总数据吞吐量通常是必要的，特别是在使用非常大的阵列时。然而，哪些信息可能丢失取决于解码器级别的处理任务。在本文中，我们研究了将麦克风信号线性下混到减少通道数量的简单方法，使用从漫射噪声模型中导出的非自适应系数，仅基于阵列的几何形状。在信源分离实验中，该复用方案即使在传输信道数量大幅减少的情况下也没有明显的质量损失，并且优于随机系数复用方案。此外，它还引入了一些关于麦克风增益和源角度的鲁棒性。
Reducing the total data throughput for microphones arrays is often necessary, especially when using very large arrays. However, what information can be lost depends on the processing task at the decoder level. In this paper, we investigate simple ways of linearly down-mixing the microphone signals into a reduced number of channels, using non-adaptive coefficients derived from a diffuse noise model, based only on the geometry of the array. In source separation experiments, this multiplexing scheme provides no significant loss in quality even with a high reduction in the number of transmission channels, and outperforms a multiplexing scheme with random coefficients. It furthermore introduces some robustness with respect to the microphone gains and angle from the sources.