An Approach to Microphone Array Self-Awareness Through a Residual Model of the Covariance Matrix

Microphone arrays are commonly used to extract source signals from noisy observations. Traditional methods rely on a priori information about the source to define the target of the array and derive optimal filters for extraction. However, in challenging acoustic environments—such as those with moving sources, multiple sources, or sporadic sound events—obtaining this information is often difficult. This can lead to performance degradation or even failure in extracting the desired source. In contrast, we propose a novel framework for array processing that defines the unwanted interferences and background noise to extract the source signal. Our approach enables real-time detection of new, unseen sources, allowing the array to possess self-awareness regarding emerging signals. This is achieved by calculating a residual model of the covariance matrix of the array observations, given the coherence matrices of the interferences and background noise. We introduce a two-stage method for calculating this residual model: the first stage computes the total coherence matrix of the interferences and noise, while the second stage determines the array's self-awareness, represented by the Wiener filter for extracting the new source. The residual model's amplitude and phase are constructed separately; the amplitude is determined through a fixed-point iteration approach, while the phase is a function of the covariance matrix and the eigenvectors of the total coherence matrix. Through simulations, we demonstrate that our approach can automatically extract new emerging sources, regardless of the presence of moving sources, multiple sources, or sporadic sound events, provided that the coherence matrix of the unwanted interferences and noise is known. This work presents a significant advancement in source extraction techniques for adverse acoustic environments.