Compressive Sampling of Sound Fields Using Moving Microphones

For conventional sampling of sound-fields, the measurement in space by use of stationary microphones is impractical for high audio frequencies. Satisfying the Nyquist-Shannon sampling theorem requires a huge number of sampling points and entails other difficulties, such as the need for exact calibration and spatial positioning of a large number of microphones. Dynamic sound-field measurements involving tracked microphones may weaken this spatial sampling problem. However, for aliasing-free reconstruction, there is still the need of sampling a huge number of unknown sound-field variables. Thus in real-world applications, the trajectories may be expected to lead to underdetermined sampling problems. In this paper, we present a compressed sensing framework that allows for stable and robust sub-Nyquist sampling of sound fields by use of moving microphones.