Multi-split least-mean-square adaptive generalized sidelobe canceller for narrowband beamforming

2004 ◽  
Author(s):  
L.S. Resende ◽  
R.D. Souza ◽  
M.G. Bellanger
Keyword(s):  
Mean Square ◽  
Least Mean Square ◽  
Generalized Sidelobe Canceller ◽  
Sidelobe Canceller

Generalized Sidelobe Canceller Beamforming with Combined Postfilter and Sparse NMF for Speech Enhancement

2020 ◽  
pp. 2150014
Author(s):  
S. Siva Priyanka ◽  
T. Kishore Kumar
Keyword(s):  
Speech Enhancement ◽  
Matrix Factorization ◽  
Microphone Array ◽  
Least Mean Square ◽  
Generalized Sidelobe Canceller ◽  
Residual Noise ◽  
Enhancement Method ◽  
Sidelobe Canceller ◽  
Diffuse Noise ◽  
Non Negative Matrix Factorization

A multi-microphone array speech enhancement method using Generalized Sidelobe Canceller (GSC) beamforming with Combined Postfilter (CP) and Sparse Non-negative Matrix Factorization (SNMF) is proposed in this paper. GSC beamforming with CP and SNMF is implemented to reduce directional noise, diffuse noise, residual noise and to separate interferences in adverse environment. In this paper, the directional noise is reduced using GSC beamforming, whereas the diffuse noise in each subband is reduced with a combined postfilter using Unconstrained Frequency domain Normalized Least Mean Square (UFNLMS) algorithm. Finally, the residual noise at the output of CP is eliminated by SNMF which optimizes the noise. The performance of the proposed method is evaluated using parameters like PESQ, SSNR, STOI, SDR and LSD. The noise reduction for four and eight microphones is compared and illustrated in spectrograms. The proposed method shows better performance in terms of intelligibility and quality when compared to the existing methods in adverse environments.

scholarly journals Signed Convex Combination of Fast Convergence Algorithm to Generalized Sidelobe Canceller Beamformer for Multi-Channel Speech Enhancement

2021 ◽  
Vol 38 (3) ◽  
pp. 785-795
Author(s):  
Siva Priyanka S ◽  
Kishore Kumar T
Keyword(s):  
Computational Complexity ◽  
Noise Reduction ◽  
Real Time ◽  
Speech Enhancement ◽  
Convex Combination ◽  
Fast Convergence ◽  
Mean Square ◽  
Least Mean Square ◽  
Noisy Conditions ◽  
Sidelobe Canceller

In speech communication applications such as teleconferences, mobile phones, etc., the real-time noises degrade the desired speech quality and intelligibility. For these applications, in the case of multichannel speech enhancement, the adaptive beamforming algorithms play a major role compared to fixed beamforming algorithms. Among the adaptive beamformers, Generalized Sidelobe Canceller (GSC) beamforming with Least Mean Square (LMS) Algorithm has the least complexity but provides poor noise reduction whereas GSC beamforming with Combined LMS (CLMS) algorithm has better noise reduction performance but with high computational complexity. In order to achieve a tradeoff between noise reduction and computational complexity in real-time noisy conditions, a Signed Convex Combination of Fast Convergence (SCCFC) algorithm based GSC beamforming for multi-channel speech enhancement is proposed. This proposed SCCFC algorithm is implemented using a signed convex combination of two Fast Convergence Normalized Least Mean Square (FCNLMS) adaptive filters with different step-sizes. This improves the overall performance of the GSC beamformer in real-time noisy conditions as well as reduces the computation complexity when compared to the existing GSC algorithms. The performance of the proposed multi-channel speech enhancement system is evaluated using the standard speech processing performance metrics. The simulation results demonstrate the superiority of the proposed GSC-SCCFC beamformer over the traditional methods.

Improved least mean square adaptive filter algorithm

2013 ◽  
Vol 32 (7) ◽  
pp. 2078-2081
Author(s):  
Cheng-xi WANG ◽  
Yi-an LIU ◽  
Qiang ZHANG
Keyword(s):  
Adaptive Filter ◽  
Mean Square ◽  
Least Mean Square

scholarly journals An Effective Adaptive Combination Strategy for Distributed Learning Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5723
Author(s):  
Chundong Xu ◽  
Qinglin Li ◽  
Dongwen Ying
Keyword(s):  
Minimum Variance ◽  
Iteration Scheme ◽  
Estimation Problem ◽  
Lms Algorithm ◽  
Unbiased Estimation ◽  
Mean Square ◽  
Least Mean Square ◽  
Combination Strategy ◽  
Adaptive Combination ◽  
Diffusion Networks

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.

Adaptive decision feedback equaliser based on sparse incremental least mean square/fourth approach

Pramana ◽  
2021 ◽  
Vol 95 (3) ◽  
Author(s):  
Anjana Kumari ◽  
Yash Keju Barapatre ◽  
Swetaleena Sahoo ◽  
Sarita Nanda
Keyword(s):  
Decision Feedback ◽  
Mean Square ◽  
Least Mean Square

Design and Analysis of the Fractional-Order Complex Least Mean Square (FoCLMS) Algorithm

2021 ◽  
Author(s):  
Jawwad Ahmad ◽  
Muhammad Zubair ◽  
Syed Sajjad Hussain Rizvi ◽  
Muhammad Shafique Shaikh
Keyword(s):  
Fractional Order ◽  
Order Complex ◽  
Mean Square ◽  
Least Mean Square

scholarly journals A Robust Dual-Microphone Generalized Sidelobe Canceller Using a Bone-Conduction Sensor for Speech Enhancement

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1878
Author(s):  
Yi Zhou ◽  
Haiping Wang ◽  
Yijing Chu ◽  
Hongqing Liu
Keyword(s):  
Speech Enhancement ◽  
Speech Intelligibility ◽  
Bone Conduction ◽  
Speech Quality ◽  
Generalized Sidelobe Canceller ◽  
Spatially Distributed ◽  
Interference Signals ◽  
Adaptive Noise ◽  
Adaptive Noise Canceller ◽  
Sidelobe Canceller

The use of multiple spatially distributed microphones allows performing spatial filtering along with conventional temporal filtering, which can better reject the interference signals, leading to an overall improvement of the speech quality. In this paper, we propose a novel dual-microphone generalized sidelobe canceller (GSC) algorithm assisted by a bone-conduction (BC) sensor for speech enhancement, which is named BC-assisted GSC (BCA-GSC) algorithm. The BC sensor is relatively insensitive to the ambient noise compared to the conventional air-conduction (AC) microphone. Hence, BC speech can be analyzed to generate very accurate voice activity detection (VAD), even in a high noise environment. The proposed algorithm incorporates the VAD information obtained by the BC speech into the adaptive blocking matrix (ABM) and adaptive noise canceller (ANC) in GSC. By using VAD to control ABM and combining VAD with signal-to-interference ratio (SIR) to control ANC, the proposed method could suppress interferences and improve the overall performance of GSC significantly. It is verified by experiments that the proposed GSC system not only improves speech quality remarkably but also boosts speech intelligibility.

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