Adaptive Pre-whitening Based on Parametric NMF
Alfredo Esquivel Jaramillo, Jesper Kjær Nielsen, Mads Græsbøll Christensen · 2019
Several speech processing methods assume that a clean signal is observed in white Gaussian noise (WGN). An argument against those methods is that the WGN assumption is not valid in many real acoustic scenarios. To take into account the coloured nature of the noise, a pre-whitening filter which renders the background noise closer to white can be applied. This paper introduces an adaptive pre-whitener based on a supervised non-negative matrix factorization (NMF), in which a pre-trained dictionary includes parametrized spectral information about the noise and speech sources in the form of autoregressive (AR) coefficients. Results show that the noise can get closer to white, in comparison to pre-whiteners based on conventional noise power spectral density (PSD) estimates such as minimum statistics and MMSE. A better pitch estimation accuracy can be achieved as well. Speech enhancement based on the WGN assumption shows a similar performance to the conventional enhancement which makes use of the background noise PSD estimate, which reveals that the proposed pre-whitener can preserve the signal of interest.