Reconstructing the Dynamic Directivity of Unconstrained Speech

An accurate model of natural speech directivity is an important step toward achieving realistic vocal presence within a virtual communication setting. In this article, we propose a method to estimate and reconstruct the spatial energy distribution pattern of natural, unconstrained speech. We detail our method in two stages. Using recordings of speech captured by a real, static microphone array, we create a virtual array that tracks with the movement of the speaker over time. We use this egocentric virtual array to measure and encode the high-resolution directivity pattern of the speech signal as it dynamically evolves with natural speech and movement. Utilizing this encoded directivity representation, we train a machine learning model that leverages to estimate the full, dynamic directivity pattern when given a limited set of speech signals, as would be the case when speech is recorded using the microphones on a head-mounted display (HMD). We examine a variety of model architectures and training paradigms, and discuss the utility and practicality of each implementation. Our results demonstrate that neural networks can be used to regress from limited speech information to an accurate, dynamic estimation of the full directivity pattern.

Deep Impulse Responses: Estimating and Parameterizing Filters with Deep Networks

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.