IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
TechTalks from event: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
Upscaling Ambisonic Sound Scenes Using Compressed Sensing TechniquesThis paper considers the application of compressed sensing to spherical acoustics in order to improve spatial sound field reconstruction. More specifically, we apply compressed sensing techniques to a set of Ambisonic sound signals to obtain a super-resolution plane-wave decomposition of the original sound field. That is to say, we investigate using the plane-wave decomposition to increase the spherical harmonic order of the Ambisonic sound scene. We refer to this as upscaling the Ambisonic sound scene. A focus of the paper is using sub-band analysis to make the plane-wave decomposition more robust. Results show that the sub-band analysis does indeed improve the robustness of the planewave decomposition when dominant overlapping sources are present or in noisy or diffuse sound conditions. Upscaling Ambisonic sound scenes allows more loudspeakers to be used for spatial sound field reconstruction, resulting in a larger sweet spot and improved sound quality.
Design of Transform Filter for Sound Field Reproduction Using Microphone Array and Loudspeaker ArrayIn this paper, we propose a novel method of sound field reproduction using a microphone array and loudspeaker array. Our objective is to obtain the driving signal of a planar or linear loudspeaker array only from the sound pressure distribution acquired by the planar or linear microphone array. In this study, we derive a formulation of the transform from the received signals of the microphone array to the driving signals of the loudspeaker array. The transform is achieved as a mean of a filter in a spatio-temporal frequency domain. Numerical simulation results are presented to compare the proposed method with the method based on the conventional least square algorithm. The reproduction accuracies were found to be almost the same, however, the filter size and amount of calculation required for the proposed method were much smaller than those for the least square algorithm based one.