| Paper ID | AUD-28.2 |
| Paper Title |
SPARSE RECOVERY BEAMFORMING AND UPSCALING IN THE RAY SPACE |
| Authors |
Shiduo Yu, Craig Jin, University of Sydney, Australia; Fabio Antonacci, Augusto Sarti, Polytechnic University of Milan, Italy |
| Session | AUD-28: Acoustic Sensor Array Processing 2: Beamforming |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Audio and Acoustic Signal Processing: [AUD-ASAP] Acoustic Sensor Array Processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
We have been exploring the integration of sparse recovery methods into the ray space transform over the past years and now demonstrate the potential and benefits of beamforming and upscaling signals in the integrated ray space and sparse recovery domain. A primary advantage of the ray space approach derives from its robust ability to integrate information from multiple arrays and viewpoints. Nonetheless, for a given viewpoint, the ray space technique requires a dense array that can be divided into sub-arrays enabling the plenacoustic approach to signal processing. In this work, we explore a method to upscale an array beyond the limits imposed by the inter-microphone distances associated with the array and the concomitant spatial aliasing. In other words, sparse recovery enables one to synthesize or interpolate signals corresponding to an array with a greater number of microphones with a smaller inter-microphone distance. A critical issue is whether or not this interpolative synthesis actually improves array signal processing. This work shows that upscaling signals in the integrated ray space and sparse recovery domain can improve both source localization and separation. |
