Impulse response estimation in high noise and in-the-wild settings, with minimal control of the underlying data distributions, is a challenging problem. We propose a novel framework for parameterizing and estimating impulse responses based on recent advances in neural representation learning. Our framework is driven by a carefully designed neural network that jointly estimates the impulse response and the (apriori unknown) spectral noise characteristics of an observed signal given the source signal. We demonstrate robustness in estimation, even under low signal-to-noise ratios, and show strong results when learning from spatio-temporal real-world speech data. Our framework provides a natural way to interpolate impulse responses on a spatial grid, while also allowing for efficiently compressing and storing them for real-time rendering applications in augmented and virtual reality.

翻译：高噪声和电离环境中的脉冲响应估计,对基本数据分布的最小控制,是一个具有挑战性的问题。我们提出了一个基于神经代表性学习最新进展的参数化和估计脉冲反应的新框架。我们的框架是由一个精心设计的神经网络驱动的,该网络联合估计脉冲反应和(主要未知的)观测信号的光谱噪声特性,并获得源信号。我们显示,即使在低信号对噪音比率下,在估计方面也很稳健,在学习spatio-时空现实世界语音数据时也显示出强有力的结果。我们的框架为在空间电网上对脉冲反应进行内插提供了自然的途径,同时也允许有效地压缩和储存这些神经网络,以便实时将应用转化为扩大和虚拟现实。