Primary-Ambient Extraction Using Ambient Phase Estimate Under Joint Sparsity and Independence Constraints for Stereo Signals

Abstract

Primary-ambient extraction (PAE) is a technique to enhance the user listening experience in spatial audio reproduction. This is achieved by extracting the primary and ambient components from the sound scene. The PAE approach of ambient phase estimation with a sparsity constraint (APES) leverages the magnitude consistency of ambient components and the sparsity of the primary components to refine the PAE performance. This approach demonstrates an improved extraction accuracy when the ambient component is relatively strong. However, APES suffers from severe extraction errors when the primary amplitudes are equal in two channels of a stereo signal, which is a common sound scene in stereo signals. In this paper, the limitations of APES are analyzed, and a novel ambient phase estimation method is proposed under the joint constraints of sparsity and independence, called APESI. This method uses the independence between the primary component and the ambient component to correct the ambient phase estimation condition. Both objective and subjective experimental results demonstrate that the proposed APESI outperforms the APES and other traditional approaches in terms of extraction accuracy and ambient spatial accuracy, especially when the primary amplitudes are equal.