Enhanced noise suppression in microphone arrays using a dynamic blocking matrix and LMS-based beamforming

In complex acoustic environments, signal processing faces numerous challenges such as noise suppression, particularly in multi-channel microphone arrays. Traditional noise reduction methods often fail to achieve ideal results in dynamic noise environments and adapt to the dynamic changes. Aiming at noise suppression in multi-channel microphone arrays, this paper proposes a method that combines a dynamic blocking matrix with beamforming and the Least Mean Square (LMS) algorithm. The experimental results of this paper show that it can increase the Signal-to-Noise Ratio (SNR) from the original 0.7 dB to 15.9 dB. Moreover, compared with the traditional GJBM blocking matrix, it has better noise reduction performance. This algorithm can be widely applied to scenarios that require high-quality signal processing in complex acoustic environments.

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Publication Details

Journal
IET conference proceedings.
Published
2025-08-11
DOI
https://doi.org/10.1049/icp.2025.2459
Citations
1
Primary Topic
Speech and Audio Processing
Type
article
Field-Weighted Citation Impact
1.49
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article

Enhanced noise suppression in microphone arrays using a dynamic blocking matrix and LMS-based beamforming

Airan Li
1 citations
IET conference proceedings.
Speech and Audio Processing
1.49
article

Enhanced noise suppression in microphone arrays using a dynamic blocking matrix and LMS-based beamforming

Airan Li
article en
1 citations

Abstract

In complex acoustic environments, signal processing faces numerous challenges such as noise suppression, particularly in multi-channel microphone arrays. Traditional noise reduction methods often fail to achieve ideal results in dynamic noise environments and adapt to the dynamic changes. Aiming at noise suppression in multi-channel microphone arrays, this paper proposes a method that combines a dynamic blocking matrix with beamforming and the Least Mean Square (LMS) algorithm. The experimental results of this paper show that it can increase the Signal-to-Noise Ratio (SNR) from the original 0.7 dB to 15.9 dB. Moreover, compared with the traditional GJBM blocking matrix, it has better noise reduction performance. This algorithm can be widely applied to scenarios that require high-quality signal processing in complex acoustic environments.

IET conference proceedings.Vol. 2025(12)
Northeastern University (CN)
Affordable and clean energy
Openalex Percentile: Top 16%
Speech and Audio Processing
1.49
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