An Adaptive Autoregressive Pre-whitener for Speech and Acoustic Signals based on Parametric NMF

A common assumption in many speech and acoustic processing methods is that the noise is white and Gaussian (WGN). Although making this assumption results in simple and computationally attractive methods, the assumption is often too simple and crude in many applications. In this paper, we introduce a general purpose and online pre-whitener which can be used as a pre-processor with methods based on the WGN assumption, improving their reliability and performance in applications with colored noise. The pre-whitener is a time-varying filter whose coefficients are found using a parametric non-negative matrix factorization (NMF), based on autoregressive (AR) mixture modeling of both the noise component and the signal component constituting the noisy signal. Compared to other types of pre-whiteners, we show that the proposed pre-whitener has the best performance, especially in applications with non-stationary noise. We also perform a large number of experiments to quantify the benefits of using a pre-whitener as a pre-processor for methods based on the WGN-assumption. The applications of interest were pitch estimation and time-of-arrival (TOA) estimation, where the WGN assumption is very popular.