Real-time, adaptive microphone subset selection for a 128-elementarray

Abstract

Some advantages of high-sensor count microphone arrays include gain over random noise, increased multiple-source resolution, and potential increases in effective frequency bandwidth. But in real-time applications, computational capabilities of embedded computing hardware might limit the number data channels that can be processed without data loss. For example, given a specific processing pipeline and sampling rate, perhaps only 24 of 128 channels can be supported in real-time. Therefore, the question of which 24-element subset of the 128 possible elements should be being used to detect and track multiple moving acoustic sources. In this presentation, principles of genetic search algorithms are used to develop a method to adaptively select optimal subsets of sensors as data is being processed. Discussion concerning optimality criteria, types of genetic algorithms, and algorithm performance will be presented as well as results corresponding to a 3-D 128-element array of digital MEMS microphones when multiple types of moving acoustic sources were nearby.