Noise reduction in infrasound signals based on mask coefficient binary weighting – Generalized cross correlation – Non-negative matrix factorization algorithm

Abstract

Infrasound noise reduction has two difficulties: the extremely limited datasets and the overlap of frequency bands. To overcome these problems, we present an algorithm, named MCWGCC-NMF, for noise reduction in multi-sensor recordings of mixture infrasound signals. The method is based on non-negative matrix factorization (NMF), and it is combined with spatial information estimated by generalized cross correlation (GCC) and mask coefficient binary weighting (MCW). The source dictionary masking by GCC-NMF, which uses spatial information of individual NMF atoms, is performed by frequency weighting and threshold selection to alleviate the frequency overlap. The results show that the proposed method achieves consistently better performance than the GCC-NMF on the infrasound dataset. Our study provides a signal processing method of infrasound noise reduction, which has the potential to further enhance the signals reception of infrasound arrays with wind noise reduction system (WNRS).