Abstract
The conventional digital signal processing dictates that a signal must be sampled at Nyquist rate or higher (over-sampling) in order to be represented without error. For multi-channel microphone arrays, it calls for the same bandwidth for each of the channels. Not only good sensors are needed, but many channels of A/D converters are required. This is wasteful of sensing and data acquisition resources. Over the past years, a new theory of Compressive Sensing Processing (CSP) has begun to emerge [1] [4] [5], in which the signal is sampled and compressed simultaneously at a greatly reduced rate. Exciting new theory and applications are popping up in analog-to-digital conversion, image processing and other areas. This paper will explore the theoretical foundations and emerging applications of Compressive Sensing. We are especially interested in low cost, high channel-count applications in acoustic applications. In this paper, an innovative design is proposed for noise source identification. The hardware required is a single microphone sensor that utilizes mechanical multi-gate array with deterministic or random opening sequences. The analog to digital conversion is accomplished by a high speed single-channel A/D converter. Since only one sensor and one A/D channel are needed, the electronic circuit and hence the associated cost can be reduced. The computational results are checked with known sources to make sure the algorithm is working.