Integration of Spatial Cue-Based Noise Reduction and Speech Model-Based Source Restoration for Real Time Speech Enhancement

2017 ◽  
Vol E100.A (5) ◽  
pp. 1127-1136 ◽  
Author(s):  
Tomoko KAWASE ◽  
Kenta NIWA ◽  
Masakiyo FUJIMOTO ◽  
Kazunori KOBAYASHI ◽  
Shoko ARAKI ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Real Time ◽  
Speech Enhancement ◽  
Model Based ◽  
Speech Model

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.

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