Primary-Ambient Extraction Using Ambient Spectrum Estimation for Immersive Spatial Audio Reproduction

The diversity of today´s playback systems requires a flexible, efficient, and immersive reproduction of sound scenes in digital media. Spatial audio reproduction based on primary-ambient extraction (PAE) fulfills this objective, where accurate extraction of primary and ambient components from sound mixtures in channel-based audio is crucial. Severe extraction error was found in existing PAE approaches when dealing with sound mixtures that contain a relatively strong ambient component, a commonly encountered case in the sound scenes of digital media. In this paper, we propose a novel ambient spectrum estimation (ASE) framework to improve the performance of PAE. The ASE framework exploits the equal magnitude of the uncorrelated ambient components in two channels of a stereo signal, and reformulates the PAE problem into the problem of estimating either ambient phase or magnitude. In particular, we take advantage of the sparse characteristic of the primary components to derive sparse solutions for ASE based PAE, together with an approximate solution that can significantly reduce the computational cost. Our objective and subjective experimental results demonstrate that the proposed ASE approaches significantly outperform existing approaches, especially when the ambient component is relatively strong.