Abstract
The noise covariance matrix computed between the signals from a microphone array is used in the design of spatial filters and beamformers with applications in noise suppression and dereverberation. This paper specifically addresses the problem of estimating the covariance matrix associated with a noise field when the array is rotating during desired source activity, as is common in head-mounted arrays. We propose a parametric model that leads to an analytical expression for the microphone signal covariance as a function of the array orientation and array manifold. An algorithm for estimating the model parameters during noise-only segments is proposed and the performance shown to be improved, rather than degraded, by array rotation. The stored model parameters can then be used to update the covariance matrix to account for the effects of any array rotation that occurs when the desired source is active. The proposed method is evaluated in terms of the Frobenius norm of the error in the estimated covariance matrix and of the noise reduction performance of a minimum variance distortionless response beamformer. In simulation experiments the proposed method achieves 18 dB lower error in the estimated noise covariance matrix than a conventional recursive averaging approach and results in noise reduction which is within 0.05 dB of an oracle beamformer using the ground truth noise covariance matrix.
Highlights
S PATIAL filtering is a fundamental tool for multichannel signal enhancement in noisy and reverberant environments and is used in many applications, such as telecommunications, automatic speech recognition, human-robot interaction, assistive listening devices and hearing aids
We propose an online algorithm based on exponentially-weighted least squares (EWLS) for estimating Rx and Ru jointly
Experiment 2 demonstrates the convergence of the estimated noise covariance matrix (NCM) compared to conventional signal dependent and independent methods, highlighting in particular the case when array rotation is in response to desired source activity
Summary
S PATIAL filtering is a fundamental tool for multichannel signal enhancement in noisy and reverberant environments and is used in many applications, such as telecommunications, automatic speech recognition, human-robot interaction, assistive listening devices and hearing aids. The widely used minimum variance distortionless response (MVDR) beamformer [1] requires knowlege of two quantities: the steering vector, which defines the distortionless constraint, and the noise covariance matrix (NCM), which describes the interchannel relationship of the undesired signal. The focus of this contribution is the Manuscript received April 11, 2018; revised September 14, 2018 and November 13, 2018; accepted November 13, 2018. Date of publication November 19, 2018; date of current version December 28, 2018. The associate editor coordinating the review of this manuscript and approving it for publication was Prof.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE/ACM Transactions on Audio, Speech, and Language Processing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
