Performance analysis of a crosstalk resistant adaptive noise canceller

1996 ◽  
Vol 43 (7) ◽  
pp. 473-482 ◽  
Author(s):  
V. Parsa ◽  
P.A. Parker ◽  
R.N. Scott
Keyword(s):  
Performance Analysis ◽  
Adaptive Noise ◽  
Adaptive Noise Canceller

scholarly journals Performance Analysis of Adaptive Noise Canceller Employing NLMS Algorithm

2015 ◽  
Vol 7 (2) ◽  
pp. 45-58 ◽  
Author(s):  
Farhana Afroz ◽  
Asadul Huq ◽  
Ahmed F ◽  
Kumbesan Sandrasegaran
Keyword(s):  
Performance Analysis ◽  
Nlms Algorithm ◽  
Adaptive Noise ◽  
Adaptive Noise Canceller

Performance Analysis of MSFRLS-VFF Based Real-Time Adaptive Noise Canceller with RLS and APA Algorithms Using TMS320C6713 Processor

2018 ◽  
Vol 106 (4) ◽  
pp. 2113-2127
Author(s):  
Deepak Kumar Gupta ◽  
Vijay Kumar Gupta ◽  
Mahesh Chandra ◽  
Bishwajeet Pandey
Keyword(s):  
Performance Analysis ◽  
Real Time ◽  
Adaptive Noise ◽  
Adaptive Noise Canceller

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.

scholarly journals Dual-Mic Speech Enhancement Based on TF-GSC with Leakage Suppression and Signal Recovery

2021 ◽  
Vol 11 (6) ◽  
pp. 2816
Author(s):  
Hansol Kim ◽  
Jong Won Shin
Keyword(s):  
Speech Enhancement ◽  
Wiener Filter ◽  
Signal Recovery ◽  
Gain Function ◽  
Microphone Signal ◽  
Perceptual Evaluation ◽  
Blocking Matrix ◽  
Adaptive Noise ◽  
Adaptive Noise Canceller ◽  
Sidelobe Canceller

The transfer function-generalized sidelobe canceller (TF-GSC) is one of the most popular structures for the adaptive beamformer used in multi-channel speech enhancement. Although the TF-GSC has shown decent performance, a certain amount of steering error is inevitable, which causes leakage of speech components through the blocking matrix (BM) and distortion in the fixed beamformer (FBF) output. In this paper, we propose to suppress the leaked signal in the output of the BM and restore the desired signal in the FBF output of the TF-GSC. To reduce the risk of attenuating speech in the adaptive noise canceller (ANC), the speech component in the output of the BM is suppressed by applying a gain function similar to the square-root Wiener filter, assuming that a certain portion of the desired speech should be leaked into the BM output. Additionally, we propose to restore the attenuated desired signal in the FBF output by adding some of the microphone signal components back, depending on how microphone signals are related to the FBF and BM outputs. The experimental results showed that the proposed TF-GSC outperformed conventional TF-GSC in terms of the perceptual evaluation of speech quality (PESQ) scores under various noise conditions and the direction of arrivals for the desired and interfering sources.

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