Linear Estimation-Based Primary Ambient Extraction

Abstract

Audio signals for moving pictures and video games are often linear combinations of primary and ambient components. In spatial audio analysis–synthesis, these mixed signals are usually decomposed into primary and ambient components to facilitate flexible spatial rendering and enhancement. Existing approaches such as principal component analysis (PCA) and least squares (LS) are widely used to perform this decomposition from stereo signals. However, the performance of these approaches in primary ambient extraction (PAE) has not been well studied, and no comparative analysis among the existing approaches has been carried out so far. In this paper, we generalize the existing approaches into a linear estimation framework. Under this framework, we propose a series of performance measures to identify the components that contribute to the extraction error. Based on the generalized linear estimation framework and our proposed performance measures, a comparative study and experimental testing of the linear estimation-based PAE approaches including existing PCA, LS, and three proposed variant LS approaches are presented.