A variable step-size multichannel equalization algorithm exploiting sparseness measure for room acoustics

Rajan S. Rashobh; Andy W.H. Khong

doi:10.1109/ISCAS.2012.6271879

A variable step-size multichannel equalization algorithm exploiting sparseness measure for room acoustics

Rajan S. Rashobh^*, Andy W.H. Khong

^*Corresponding author for this work

Nanyang Technological University

Research output: Contribution to conference › Paper › peer-review

4 Citations (Scopus)

Abstract

Non-adaptive multichannel equalization (MCEQ) algorithms based on multiple input/output inverse theorem (MINT) is computationally expensive as MINT involves the inversion of a convolution matrix with dimension that is proportional to the length of the acoustic impulse responses. To address this, we propose a MINT-based algorithm that estimates inverse filters by minimizing a cost function iteratively. To further enhance the convergence rate, we formulate an algorithm that employs an adaptive step-size that is derived as a function of the sparseness measure. The proposed algorithm is then applied to existing MINT-based equalization algorithms such as A-MINT and the currently proposed MCEQ-based algorithms.

Original language	English
Pages	2753-2756
Number of pages	4
DOIs	https://doi.org/10.1109/ISCAS.2012.6271879
Publication status	Published - 2012
Externally published	Yes
Event	2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012 - Seoul, Korea, Republic of Duration: May 20 2012 → May 23 2012

Conference

Conference	2012 IEEE International Symposium on Circuits and Systems, ISCAS 2012
Country/Territory	Korea, Republic of
City	Seoul
Period	5/20/12 → 5/23/12

ASJC Scopus Subject Areas

Hardware and Architecture
Electrical and Electronic Engineering

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10.1109/ISCAS.2012.6271879

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abstract = "Non-adaptive multichannel equalization (MCEQ) algorithms based on multiple input/output inverse theorem (MINT) is computationally expensive as MINT involves the inversion of a convolution matrix with dimension that is proportional to the length of the acoustic impulse responses. To address this, we propose a MINT-based algorithm that estimates inverse filters by minimizing a cost function iteratively. To further enhance the convergence rate, we formulate an algorithm that employs an adaptive step-size that is derived as a function of the sparseness measure. The proposed algorithm is then applied to existing MINT-based equalization algorithms such as A-MINT and the currently proposed MCEQ-based algorithms.",

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AB - Non-adaptive multichannel equalization (MCEQ) algorithms based on multiple input/output inverse theorem (MINT) is computationally expensive as MINT involves the inversion of a convolution matrix with dimension that is proportional to the length of the acoustic impulse responses. To address this, we propose a MINT-based algorithm that estimates inverse filters by minimizing a cost function iteratively. To further enhance the convergence rate, we formulate an algorithm that employs an adaptive step-size that is derived as a function of the sparseness measure. The proposed algorithm is then applied to existing MINT-based equalization algorithms such as A-MINT and the currently proposed MCEQ-based algorithms.

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A variable step-size multichannel equalization algorithm exploiting sparseness measure for room acoustics

Abstract

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ASJC Scopus Subject Areas

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