A hybrid nonlinear adaptive noise canceller for fetal ECG extraction

Fetal electrocardiogram (FECG) non-invasively obtained through abdominal recordings serves as a promising diagnostic tool for fetal health monitoring during pregnancy. However, in the abdominal ECG (AECG) signal, FECG overlaps with maternal ECG (MECG) in both temporal and spectral domains in addition to interference from various sources like electromyogram, electrogastrogram, motion artifacts and other noises. The objective of this paper is to eliminate MECG components from AECG signal to extract FECG signal through FIR adaptive noise canceller (ANC) with filter coefficients updated using adaptive algorithms. Adaptive filters are suitable for current problem of interest and Least Mean Square (LMS) and its variants are analyzed for the problem of FECG extraction. We have compared the four variants of LMS such as normalized LMS (NLMS), sign-error algorithm, least mean fourth (LMF) algorithms for FECG extraction. The algorithms are evaluated using real-time abdominal ECG recordings acquired from daisy database. The performance of each algorithm is evaluated using various parameters like sensitivity, accuracy, positive predictive values and [Formula: see text] score. Further, the convergence rate for different algorithms are plotted and analyzed. From the simulation results, it is observed that the LMF algorithm outperforms its counterparts by providing an accuracy and positive predictive value of 73.3%, sensitivity of 100% and [Formula: see text] measure of 84.5%. The convergence plots obtained justify that LMF algorithm has a faster convergence rate compared to the other variants of LMS.

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Multiple Sub-Filter Adaptive Noise Canceller for Fetal ECG Extraction

Procedia Computer Science ◽

10.1016/j.procs.2020.01.093 ◽

2019 ◽

Vol 165 ◽

pp. 182-188

Author(s):

Jaba Deva Krupa Abel ◽

Dhanalakshmi Samiappan ◽

R Kumar ◽

S Pravin Kumar

Keyword(s):

Fetal Ecg ◽

Adaptive Noise ◽

Adaptive Noise Canceller

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Car Noise Suppression Using Adaptive Noise Canceller with Speech Suppressors

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.136.1218 ◽

2016 ◽

Vol 136 (8) ◽

pp. 1218-1229

Author(s):

Yuya Honda ◽

Arata Kawamura ◽

Youji Iiguni

Keyword(s):

Noise Suppression ◽

Adaptive Noise ◽

Adaptive Noise Canceller

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

Sensors ◽

10.3390/s21051878 ◽

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

Applied Sciences ◽

10.3390/app11062816 ◽

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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