A class of frequency-domain adaptive approaches to blind multichannel identification
Abstract
We extend our previous studies on adaptive blind channel identification from the time domain into the frequency domain. A class of frequency-domain adaptive approaches, including the multichannel frequency-domain LMS (MCFLMS) and constrained/unconstrained normalized multichannel frequency-domain LMS (NMCFLMS) algorithms, are proposed. By utilizing the fast Fourier transform (FFT) and overlap-save techniques, the convolution and correlation operations that are computationally intensive when performed by the time-domain multichannel LMS (MCLMS) or multichannel Newton (MCN) methods are efficiently implemented in the frequency domain, and the MCFLMS is rigorously derived. In order to achieve independent and uniform convergence for each filter coefficient and, therefore, accelerate the overall convergence, the coefficient updates are properly normalized at each iteration, and the NMCFLMS algorithms are developed. Simulations show that the frequency-domain adaptive approaches perform as well as or better than their time-domain counterparts and the cross-relation (CR) batch method in most practical cases. It is remarkable that for a three-channel acoustic system with long impulse responses (256 taps in each channel) excited by a male speech signal, only the proposed NMCFLMS algorithm succeeds in determining a reasonably accurate channel estimate, which is good enough for applications such as time delay estimation.
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The NMCFLMS algorithm accelerates the convergence rate of the MCLMS algorithm.
“To accelerate the convergence rate, a normalized multichannel frequency-domain LMS (NMCFLMS) algorithm was developed in [25].”
From Time Delay Estimation in Room Acoustic Environments: An Overview (2006) · cites this paper as [25]
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Self-citationCho, J.-W., & Park, H. (2009) · IEEE Signal Processing Letters · doi:10.1109/lsp.2008.2012222 · Cited by 8 · see citing works
Non-cooperative MPSK Modulation Identification in SIMO Underwater Acoustic Multipath Channel
Fang, T., Liu, S., Wu, X., Yan, H., & Khan, I. U. (2021) · IEEE OES China Ocean Acoustics (COA) · doi:10.1109/coa50123.2021.9520023 · Cited by 7 · see citing works
A State-Space Cross-Relation Approach to Adaptive Blind SIMO System Identification
Malik, S., Schmid, D., & Enzner, G. (2012) · IEEE Signal Processing Letters · doi:10.1109/lsp.2012.2204873 · Cited by 7 · see citing works
Speech Dereverberation Performance of a Polynomial-EVD Subspace Approach
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Blind Estimation of Spatial Room Impulse Responses Using a Pseudo Reference Signal
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Statistical modelling of multichannel blind system identification errors
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Dual-microphone speech dereverberation using GARCH modeling
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- Authors
- Huang, Y., & Benesty, J.
- ORCID
- Benesty, J.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2003-01
- Volume
- 51
- Issue
- 1
- Pages
- 11-24
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2110097273
- Cited by
- 199
- References
- 22
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 3 · 2023: 3 · 2022: 6 · 2021: 4 · 2020: 6 · 2019: 4 · 2018: 7 · 2017: 6 · 2016: 8 · 2015: 15 · 2014: 21 · 2013: 11 · 2012: 16
- FWCI
- 11.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Signal Processing (publishedVersion)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › Advanced Adaptive Filtering Techniques
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- blind channel estimation, multichannel adaptive filtering, fast Fourier transform, convergence acceleration, time delay estimation
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