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Publication

Direct-Ambient Decomposition and Upmix of Surround Signals

Christof Faller, Andreas Walther
2011
Conference Papers

Abstract

Various direct-ambient signal decompositions, applicable to two-channel stereophonic signals, have been proposed previously. Strategies to enhance these decompositions for multi-channel surround audio signals are not obvious. A method is proposed, which estimates direct and ambient signal components in a downmix of a given surround signal. Those estimates are then used to compute filters to obtain direct and ambient surround signals. Approaches on how to use the decomposed signals for upmixing of surround audio signals in two and three dimensions are also discussed.

Official source

https://infoscience.epfl.ch/entities/publication/52fde567-847f-4522-8311-44b29085b469

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Christof Faller, Andreas Walther
2011
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/52fde567-847f-4522-8311-44b29085b469

About this result

Related concepts (19)

Surround sound

Surround sound is a technique for enriching the fidelity and depth of sound reproduction by using multiple audio channels from speakers that surround the listener (surround channels). Its first application was in movie theaters. Prior to surround sound, theater sound systems commonly had three screen channels of sound that played from three loudspeakers (left, center, and right) located in front of the audience.

Matrix decomposition

In the mathematical discipline of linear algebra, a matrix decomposition or matrix factorization is a factorization of a matrix into a product of matrices. There are many different matrix decompositions; each finds use among a particular class of problems. In numerical analysis, different decompositions are used to implement efficient matrix algorithms. For instance, when solving a system of linear equations , the matrix A can be decomposed via the LU decomposition.

QR decomposition

In linear algebra, a QR decomposition, also known as a QR factorization or QU factorization, is a decomposition of a matrix A into a product A = QR of an orthonormal matrix Q and an upper triangular matrix R. QR decomposition is often used to solve the linear least squares problem and is the basis for a particular eigenvalue algorithm, the QR algorithm. Any real square matrix A may be decomposed as where Q is an orthogonal matrix (its columns are orthogonal unit vectors meaning ) and R is an upper triangular matrix (also called right triangular matrix).

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