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An environment aware ML estimation of acoustic radiation pattern with distributed microphone pairs

机译：分布式麦克风对在环境中的声辐射图的ML估计

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摘要

This paper presents a parametric approach to classify the radiation pattern of an acoustic source given the signals captured by multiple microphones. The radiation pattern influences the way the acoustic waves propagate within an enclosure, with direct implications on the behavior of most audio processing algorithms. In particular, the Generalized Cross-Correlation PHAse Transform is affected by the emission pattern as well as by the orientation of the source. A Maximum Likelihood estimator is introduced by using descriptors of the acoustic characteristics of the environment, e.g. wall absorption coefficients and room dimensions, from which models of the observed Generalized Cross-Correlation PHAse Transform are derived for a specific emission pattern. A generic unimodal source directivity is modeled using a parameterized cardioid function. A sub-band implementation is proposed to account for the frequency dependence of the source emission pattern. Experiments on simulated and real data show that the acoustic radiation pattern can be estimated in an effective way under noisy and reverberant conditions.
机译：本文提出了一种参数化方法，可根据多个麦克风捕获的信号对声源的辐射方向图进行分类。辐射方向图会影响声波在外壳中传播的方式，直接影响大多数音频处理算法的行为。特别是，广义互相关PHAse变换受发射模式以及源方向的影响。通过使用环境声学特性的描述符（例如，最大似然率）引入最大似然估计器。壁吸收系数和房间尺寸，从中得出观察到的广义互相关PHAse变换模型以用于特定的发射模式。通用单峰源方向性使用参数化心形函数建模。提出了一个子带实施方案来考虑源发射模式的频率依赖性。模拟和真实数据的实验表明，在噪声和混响条件下，可以有效地估计声辐射方向图。

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