Primary-Ambient Extraction Using Ambient Phase Estimation with a Sparsity Constraint

Spatial audio reproduction addresses the growing commercial need to recreate an immersive listening experience of digital media content, such as movies and games. Primary-ambient extraction (PAE) is one of the key approaches to facilitate flexible and optimal rendering in spatial audio reproduction. Existing approaches, such as principal component analysis and time-frequency masking, often suffer from severe extraction error. This problem is more evident when the sound scene contains a relatively strong ambient component, which is frequently encountered in digital media. In this Letter, we propose a novel PAE approach by estimating the ambient phase with a sparsity constraint (APES). This approach exploits the equal magnitude of the uncorrelated ambient components in the two channels of a stereo signal and reformulates the PAE problem as an ambient phase estimation problem, which is then solved using the criterion that the primary component is sparse. Our experimental results demonstrate that the proposed approach significantly outperforms existing approaches, especially when the ambient component is relatively strong.