Improving Signal Clarity through Interference Suppression and Emergent Signal Detection
Microphone arrays have seen wide usage in a variety of fields; especially in sonar, acoustic source monitoring and localization, telecommunications, and diagnostic medicine.
The goal of most of these applications is to detect or extract a signal of interest. This task is complicated by the presence of interferers and noise, which corrupts the recorded array signals. This dissertation explores two new techniques that increase signal clarity: interferer suppression and emergent signal detection.
Spatial processing is often used to suppress interferers that are spatially distinct from the signal of interest. If the signal of interest and the interferer are statistically independent, blind source separation can be used to statistically extract the signal of interest. The first new method to improve signal clarity presented in this work combines spatial processing with blind source separation to suppress interferers. This technique allows for the separation of independent sources that are not necessarily simultaneously mixed or spatially distinct. Simulations and experiments are used to show the capability of the new algorithm for a variety of conditions. The major contributions in this dissertation under this topic are to use independent component analysis to extract the signal of interest from a set of array signals, and to improve existing independent component analysis algorithms to allow for time delayed mixing.
This dissertation presents a novel method of improving signal clarity through emergent signal detection. By determining which time frames contain the signal of interest, frames that contain only interferers and noise can be eliminated. When a new signal of interest emerges in a measurement of a mixed set of sources, the principal component subspace is altered. By examining the change in the subspace, the emergent signal can be robustly detected. This technique is highly effective for signals that have a near constant sample variance, but is successful at detecting a wide variety of signals, including voice signals. To improve performance, the algorithm uses a feed-forward processing technique. This is helpful for the VAD application because voice does not have a constant sample variance. Experiments and simulations are used to demonstrate the performance of the new technique.