论文标题
深度冲动响应：通过深网估算和参数化过滤器
Deep Impulse Responses: Estimating and Parameterizing Filters with Deep Networks
论文作者
论文摘要
高噪声和野外设置中的冲动响应估计，对基础数据分布的控制最少是一个挑战性的问题。我们提出了一个新的框架，用于基于神经表示学习的最新进展，用于参数化和估计脉冲响应。我们的框架是由经过精心设计的神经网络驱动的，该神经网络共同估计了源信号的脉冲响应和（APRIORI未知的）光谱噪声特性（APRIORI未知）。我们即使在低信噪比的比率下也证明了估计的鲁棒性，并在从时空现实世界中的语音数据中学习时会显示出很强的结果。我们的框架提供了一种自然的方法，可以在空间网格上插入冲动响应，同时还允许在增强和虚拟现实中有效地压缩和存储它们，以实时渲染应用。
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.
