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|Title:||Primary-ambient extraction using ambient phase estimation with a sparsity constraint||Authors:||He, Jianjun
|Keywords:||DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing||Issue Date:||2014||Source:||He, J., Gan, W.-S., & Tan, E-L. (2015). Primary-ambient extraction using ambient phase estimation with a sparsity constraint. IEEE signal processing letters, 22(8), 1127-1131.||Series/Report no.:||IEEE signal processing letters||Abstract:||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.||URI:||https://hdl.handle.net/10356/107464
|ISSN:||1070-9908||DOI:||http://dx.doi.org/10.1109/LSP.2014.2387021||Rights:||© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/LSP.2014.2387021].||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
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