Ray space analysis with sparse recovery
Craig T Jin, Fabio Antonacci, Augusto Sarti · 2017
This work explores integrating sparse recovery methods into the ray space transform. Sparse recovery methods have proven useful in microphone array analysis of sound fields. In particular, they can provide extremely accurate estimates of source direction in the presences of multiple, simultaneous sources and noise. The ray space transform has recently emerged as a useful tool to analyse sound fields, particulary by robustly integrating information from multiple viewpoints. In this work, we present the results of numerical simulations for a linear microphone array that demonstrate the promising improvements obtained by integrating sparse recovery into the ray space transform.