Adaptive Noise Cancellation Research Articles

Tablet-Based Wearable Patch Sensor Design for Continuous Cardiovascular System Monitoring in Postoperative Settings

Meticulous monitoring for cardiovascular systems is important for postoperative patients in postanesthesia or the intensive care unit. The continuous auscultation of heart and lung sounds can provide a valuable information for patient safety. Although numerous research projects have proposed the design of continuous cardiopulmonary monitoring devices, they primarily focused on the auscultation of heart and lung sounds and mostly served as screening tools. However, there is a lack of devices that could continuously display and monitor the derived cardiopulmonary parameters. This study presents a novel approach to address this need by proposing a bedside monitoring system that utilizes a lightweight and wearable patch sensor for continuous cardiovascular system monitoring. The heart and lung sounds were collected using a chest stethoscope and microphones, and a developed adaptive noise cancellation algorithm was implemented to remove the background noise corrupted with those sounds. Additionally, a short-distance ECG signal was acquired using electrodes and a high precision analog front end. A high-speed processing microcontroller was used to allow real-time data acquisition, processing, and display. A dedicated tablet-based software was developed to display the acquired signal waveforms and the processed cardiovascular parameters. A significant contribution of this work is the seamless integration of continuous auscultation and ECG signal acquisition, thereby enabling the real-time monitoring of cardiovascular parameters. The wearability and lightweight design of the system were achieved through the use of rigid–flex PCBs, which ensured patient comfort and ease of use. The system provides a high-quality signal acquisition and real-time monitoring of the cardiovascular parameters, thus proving its potential as a health monitoring tool.

A robust sliding window adaptive filtering technique for phonocardiogram signal denoising

AbstractObjectiveThe digital sound recording of several heart sounds (HSs) is named phonocardiogram (PCG) signal. Analysing these PCG signals is essential for diagnosing diverse sorts of heart disorders. Nevertheless, owing to troubling surrounding noise signals, PCG signal recording is challenging. So, before utilising PCG for advanced processing, de‐noising the PCG signal is executed. A new sliding window adaptive noise cancellers (SWANC)‐centred filter technique is proposed in this paper for de‐noising along with recovering the PCG signal effectively.MethodUtilising the least mean square (LMS), an SW optimum adaptive filter (AF) structure is introduced in this work for estimating a De‐noised signal (DS) with better accuracy. Here, via the SWAF stage, a noisy signal is processed. An SW of fixed duration slides over the signal; also, the signal is filtered utilising the AF in each window. Utilising this SWAF architecture, this method approximates the PCG signal's clean version with better accuracy.ResultsThe proposed robust SWAF is analogized to experimental PCG signals that were corrupted by Gaussian noise (GN) together with pink noise (PN) with distinct noise levels (NLs). From the physionet database, the experiential data are acquired. The outcomes exhibited that a remarkable performance was attained by the robust SWAF model.DiscussionA 2–10 times decrease in MSE values was attained by the proposed filter structure when analogized with conventional LMS filter configuration. Further, the SNR is improved by 3 times and comparatively, the PSNR enhancement was 4%–25%. The association betwixt the clean signal along with its estimate is greater than 0.92.ConclusionA cost‐efficient hardware installation of ANC with higher accuracy was offered by the SW LMS AF model. For obtaining desired convergence speed and accuracy, such models are tested for real‐time performances in the future.

Adaptive Active Wideband Acoustic Noise Cancellation System with Dynamic Calibration

The article contains the operational principle of adaptive active wideband acoustic noise cancellation system. The algorithm of operation of these systems provides to work with changes of the medium, acoustic or electrical parameters of their elements, is described. A modification of this algorithm is proposed, which can increase the efficiency of the system and reduce the level of residual noise during system calibration. The results of simulation the modified algorithm of an active noise cancellation system are presented.

A novel adaptive noise canceling system for urea NQR spectrum analysis

Due to its practical ease of use and sufficiently high reliability, Nuclear Quadrupole Resonance (NQR) has largely been used in remote sensing-based detection of quadrupole nuclei, especially Nitrogen-containing materials. Since the received signal of free induction decay (FID) is inherently weak in this technique, the noise and the interference of the environment would dramatically impair the performance of the detecting systems. To handle such challenges, an adaptive noise canceling method was employed to improve the signal to noise and interference ratio (SNIR) in FID signals. FID signals, noise, and interference were received by the main antenna, while similar noise and interference would be received by the auxiliary antenna. The proposed system significantly reduced noise and interference through the digital noise and interference cancellation algorithm, pulse integration, averaging, and singular spectrum analysis (SSA). Urea powder was empirically analyzed outdoors, suggesting that the proposed adaptive noise and interference cancellation (ANIC) system, effectively suppressed noise and interference by 41.13 dB and amplified the FID signal by 53.36 dB. The strongest signal was detected at 2870.4 kHz.

Prescribed-time distributed observer based practical predefined-time leader-follower output consensus of second-order multiagent system with communication noises

This paper studies predefined-time leader-follower consensus issue for second-order multi-agent system (MAS) with communication noises over digraph. Crosstalk resistant adaptive noise canceller is utilized to filter out communication noise. Next, with the filtered states received from its neighbors, a prescribed-time distributed observer is first proposed, which requires no global information and provides prescribed-time leader's states estimation under noisy communication environment and directed communication graph. Then, an adaptive predefined-time control strategy is presented to counteract the mismatched disturbance caused by estimation error and achieve practical predefined-time leader-follower output consensus of the studied MAS. Lyapunov stability analysis proves that the developed distributed observer achieves prescribed-time convergence and the developed tracking controller achieves predefined-time convergence. Finally, the developed control strategy is applied to consensus tracking of networked single-link robotic manipulators and simulation results show its effectiveness.

Development of Adaptive Spectrum Noise Cancellation Technique for Enhancing Heartbeat Rate Monitoring

Most of the Heart Rate Monitor (HRM) seems to be a portable monitoring device that can measure and displays real-time heartbeat rate in addition to storing heartbeat rate data for future studies. It is generally used to capture heart rate data during various types of physical exercise. Heart rhythm problems arise whenever the electrical impulses that coordinate the heartbeats aren't working properly. The ineffective signaling causes the heart to beat too fast (tachycardia), overly slowly (bradycardia), or sporadically. In present study, a focused approach on Adaptive Spectrum Noise Cancellation (ASNC) were used for enhancing heartbeat rate monitoring for humans. It finds that simply a photodetector and a source of light are applied to the surface of the skin to monitor the proportional changes in the blood flow. It is concluded that for accurate measurement of heart rate while moving or running, the proposed device ASNC utilizes the application of embedded accelerometer and gyroscope sensors to identify and eradicate the artifacts adaptively. In future, the proposed model may adopted for modification and commercialization of wearable heart-rate sensors based upon photoplethysmography (PPG), it would be better in terms of utility as well as adoptability.

Design Scheme of Proximal Adaptive Noise Cancellation in Shallow Hydrological Monitoring Using a Multichannel Nuclear Magnetic Resonance Logging Tool

Small diameter nuclear magnetic resonance (NMR) logging tools are used to detect hydrologic characteristics, soil moisture, and migration of petroleum hydrocarbon contaminants near-surface in the existing hydrologic monitoring wells. However, the majority of hydrologic monitoring wells are located in densely populated areas with high electromagnetic noise levels, seriously affecting the monitoring accuracy. Consequently, decreasing electromagnetic noise is a critical component in expanding the use of small-diameter NMR logging tools. In this study, a proximal adaptive noise cancellation (PANC) method was introduced for a small-diameter NMR tool. A simulation model is used to evaluate the performance of various ANC algorithms in different conditions. The effect of coil size and position on the PANC method was then analyzed. Subsequently, a static experiment is conducted to demonstrate the validity of the method. The results show that the PANC method achieves 55% noise energy suppression without affecting the target signal intensity and bandwidth.

Application of Singular Value Decomposition and Adaptive Noise Cancellation Method in AUV Sonar Pulse

The detection signal-to-noise ratio determines the underwater acoustic reconnaissance ability of AUV in complex marine environments. In this paper, the singular value decomposition method and adaptive noise cancellation method are used to improve the signal-to-noise ratio of dual channel sonar signal. The experimental data processing results show that the singular value decomposition method has a stronger noise reduction ability, but the noise reduction effect is greatly affected by the singular value threshold, and the performance of the algorithm is unstable; Although the noise reduction ability of adaptive noise cancellation method is limited, it has strong environmental adaptability and more stable algorithm performance.

Variable-scale evolutionary adaptive mode denoising in the application of gearbox early fault diagnosis

Due to the influence of gearbox structure complexity, vibration signals are always corrupted by heavy multiscale noise interference distributed in modulation frequency bands, which brings great challenge for early fault identification. However, conventional adaptive noise cancellation methods mainly focus on a comprehensive denoising effect while ignore improper reference induced by multiscale characteristics of noise interference. This will lead to two major problems. On one aspect, reference filtering at sensitive scales is not mutually independent, and it has a negative influence on each other. On the other aspect, improper reference may cause losses of fault-related information. These limitations greatly hinder the fault features identification, especially in an early fault stage of a fault. Aiming at these problems, this study proposes a novel variable-scale evolutionary adaptive mode denoising method (VEAMD) for weak feature enhancement of gearbox early fault diagnosis. First, to sense the multiscale distribution characteristics of a desired signal, an ergodic VMD process with a dual domain energy factor (DDEF) is presented, where a series of Wiener filters are adaptively designed. Secondly, the desired signal and a reference signal are input into these Wiener filters to generate multiple mode subsignals with wideband noise suppressed. Meanwhile, multiscale noise interferences are separated into different scales as reference bases for mode denoising. Third, via evolutionary digital filters (EDFs), a refined reference filtering is performed within mode scale to obtain multiple intrinsic mode subsignals (IMSs), namely mode denoising. Finally, the Pearson correlation coefficients between two parts of each subsignal pair is employed as a weight to synthesize a denoised signal. In this manner, VEAMD effectively tackles an improper reference at local scales by a series of adaptive mode denoising, achieving more significant noise cancellation capability and an effect of weak feature enhancement, which is conducive for a timely and accurate early gearbox fault diagnosis. Simulation and experiment prove the superiority of VEAMD over other four methods.

Cardiopulmonary auscultation enhancement with a two-stage noise cancellation approach

For cardiopulmonary auscultation using electronic stethoscopes, signal quality is a key point. During signal acquisition various background sounds may be inevitably captured, severely degrading the auscultation signal quality. In the existing auscultation denoising methods, conventional adaptive noise canceller (ANC) approaches or shallow-layer artificial neural networks were used, while advanced noise cancellation methods still need to be developed to fulfill real auscultation requirements. In this paper, we propose a novel denoising method for cardiopulmonary auscultation enhancement, which is a two-stage approach with a cascade of ANC and deep neural networks (DNNs). In the first stage, the ANC provides coarsely denoised auscultation signal and estimated interference. In the second stage, a DNN termed the dual-channel interactive noise cancellation network (DINC-Net) is proposed, which exploits both the coarsely denoised auscultation signal and the estimated interference. The DINC-Net consists of two deep encoders extracting features of dual-channel inputs separately, one dual-channel interactive denoising module generating a denoising mask, and one deep decoder giving the denoised output. The performance of the proposed method is evaluated through synthetic data generated using two public heart/lung sound databases, and the great promotion in normalized covariance measure (NCM) and frequency-weighted segmental signal-to-noise ratio (fwSNRseg) has been verified, compared to the existing methods. An online noise cancellation prototype is further developed on an electronic stethoscope, and the signal quality promotion is shown on healthy subjects as well as aortic stenosis patients.

Coherence Function and Adaptive Noise Cancellation Performance of an Acoustic Sensor System for Use in Detecting Coronary Artery Disease.

Adaptive noise cancellation is a useful linear technique to attenuate unwanted background noise that cannot be removed using traditional frequency-selective filters. Usually, this is due to the signal and noise co-existing in the same frequency band. This paper tests a weighted least mean squares (WLMS) algorithm on a stethoscope system for use in detecting coronary artery disease in the presence of background noise. Each stethoscope is equipped with two microphones: one used to detect heart signals and one used to detect background noise. The WLMS method was used for four different sources of background noise whilst measuring a heartbeat, including a single tone, multiple tones, hospital/clinic noise, and breathing noise. The magnitude-squared coherence between both microphones was unity for the tone scenarios, resulting in complete attenuation. For the other background noise sources, a less-than-unity magnitude-squared coherence resulted in minor and no attenuation. Thus, the coherence function is a tool that can be used to predict the amount of attenuation achievable by linear adaptive noise-cancellation techniques, such as WLMS, as presented in this article.

Dual-microphone active noise cancellation paved with Doppler assimilation for TADS

An improved dual-microphone adaptive Active Noise Cancellation (ANC) method called IM-RM-LMS is proposed to solve the problem of the bearing sound signal being interfered with by wheel-track contact noise in the TADS. Two microphones, the main microphone and the reference microphone, are used to collect the bearing acoustic signal and the wheel-track contact noise signal, respectively. Due to the different relative spatial positions of the two microphones and the noise source, the noise signal components collected by the two microphones obey different Doppler distortion laws, which makes the LMS-based ANC method ineffective. Therefore, the main research content of this paper is the ANC of two moving sound source signals that obey different Doppler distortion laws. An Inverse Modulation and Re-Modulation (IM-RM) processing is innovatively performed to assimilate the Doppler distortion law of the noise signal components collected by the two microphones. The effectiveness of IM-RM-LMS is verified by simulation and experiment. Compared with the traditional LMS algorithm without considering Doppler assimilation, it can achieve a significantly better denoising effect. This method provides a new theoretical basis for noise cancellation of two moving sound sources obeying different Doppler distortion laws and it is expected to be applied to the noise cancellation of TADS.

Adaptive Tow Ship Noise Cancellation Using Deep Regression Neural Network

This paper investigates the problem of cancellation of noise generated by own platform in shallow water scenario. In the case of underwater acoustics, the target signal detection and tracking in the presence of tow ship noise is a challenging task. A computationally intensive technique is necessary for tow ship noise suppression. In this paper, an algorithm using deep regression neural network (DRNN) along with minimum variance distortionless response (MVDR) beamformer is presented for tow ship noise cancellation. Nine DRNN’s each with different weight initialization techniques and activation functions are designed for effective tow ship noise cancellation. The designed DRNNs is tested using the simulated data and further validated using the real data collected during the trials from Arabian Sea.

Vehicle Engine Noise Cancellation Based on a Multi-Channel Fractional-Order Active Noise Control Algorithm

For the problem of low-frequency noise suppression in a large area of the vehicle space, this paper proposes a novel multi-channel fractional-order active noise control algorithm and tests its ability for vehicle engine noise cancellation. The algorithm uses fractional-order calculus to replace the integer-order gradient descent to update the weighting coefficients of the filter, thereby avoiding imbalance or over-tuning phenomena during adaptive active noise cancellation and improving the control accuracy of the algorithm. A computer simulation was conducted in the noise cancellation scene of the vehicle, which showed that the algorithm is capable of suppressing single- and mixed-frequency noises, reducing the average sound pressure by approximately 20 dB. The experimental results demonstrated that the algorithm can effectively reduce the peak sound pressure of low-frequency noise for a certain type of vehicle engine by approximately 10 dB, whereas the total sound pressure level is reduced by approximately 4.7 dB.

Joint suppression of normal gear mesh component and background noise for early local fault detection based on dynamic evolutionary digital filter

Signal denoising is one of the most essential steps in mechanical fault diagnosis, which can help improve the signal-to-noise ratio (SNR) and find faults as earlier as possible. Many denoising techniques have been developed to suppress both Gaussian white and non-Gaussian background noise. However, most of them achieved poor performance in terms of assessing local gear fault in the early stage. The key problem is that only one or several gear tooth’s stiffness or displacement excitation are aroused in the beginning of local faults and most of the gear teeth or gear pairs are healthy, especially for the multi-stage gearbox. And both the normal gear meshing signals and background noise lead to low SNR of fault characteristics. To overcome this problem, this paper proposes a dynamic evolutionary digital filter (Dyna-EDF) method to realize the optimal suppression of background noise and normal gear meshing component in the acquired signal aiming to enhance the SNR of early fault characteristics. The proposed method belongs to an adaptive noise cancellation (ANC) method, which can find and suppress the relevant component between the acquired signal and reference signal based on an iterative optimization of a digital filter. In the proposed method, a filter parameter population and ANC structure is initialized and established firstly. Then the filter parameters of the population are dynamically adjusted to find the optimal filter based on cloning and mating evolutionary strategies. Furthermore, “survival of the fittest” idea is employed for the optimal filter searching and applied during each iteration in Dyna-EDF method to prevent the premature problem that found in traditional EDF. Finally, the derived optimal filter is applied to suppress the relevant component between the acquired signal and reference signal using an ANC process. Results show that the proposed Dyna-EDF method successfully increase the SNR of fault characteristics, both normal gear mesh component and background noise are suppressed effectively. The proposed Dyna-EDF method seems achieve better denoising performance than traditional EDF method, its improved versions and the traditional denoising methods.

A robust low-cost adaptive filtering technique for phonocardiogram signal denoising

ObjectivePhonocardiogram (PCG) represents the recordings of various heart sounds. To diagnose the different ailments of the heart, it is required to analyze these PCG signals. However, recording PCG signals is challenging since it is prone to surrounding noise signals. Therefore, there is a need to denoise the PCG signal before being used for advanced processing. This paper proposes an Adaptive Noise Cancellers-based filter model for effectively denoising and recovering the PCG signal. MethodThis work introduces an optimum adaptive filter structure for estimating a noise-free signal with high accuracy using Least Mean Square (LMS) algorithm. A noisy signal is processed through multiple adaptive filter stages connected in series in the proposed work. Multiple stages are automatically added, and each stage filter’s step size process is dynamically changed. The estimate of clean PCG signal approximated using this multistage cascaded adaptive filter architecture is subsequently used in the next module to recover the clean PCG signal with high accuracy. ResultsThe proposed robust multistage adaptive filter is evaluated for denoising synthetic and experimental PCG signals corrupted by Gaussian and pink noise of various input Signal to noise (SNR) levels. The experimental data are taken from the physionet database (Classification of Heart Sound Recordings: The PhysioNet/Computing in Cardiology Challenge 2016). The results demonstrate that the robust multistage filter model performs remarkably well. DiscussionCompared with various filter configurations, the proposed filter structure achieves an 8-50% reduction in MAE values and the 45–87% reduction in MSE values. Further, there is an improved SNR of 15–60%, ANR of 15–65%, and PSNR improvement by 7–25% comparatively. The correlation between the clean signal and its estimate obtained using the proposed filter model is more than 0.99. ConclusionUsing an LMS adaptive filter in the proposed filter model offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy. In the future, the suggested robust multistage adaptive filter model can be tested for real-time performance when improved convergence speed and accuracy are desired.

Removal of multiple artifacts from ECG signal using cascaded multistage adaptive noise cancellers

Although cascaded multistage adaptive noise cancellers have been employed before by researchers for multiple artifact removal from the ElectroCardioGram (ECG) signal, they all used the same adaptive algorithm in all the cascaded multi-stages for adjusting the adaptive filter weights. In this paper, we propose a cascaded 4-stage adaptive noise canceller for the removal of four artifacts present in the ECG signal, viz. baseline wander, motion artifacts, muscle artifacts, and 60 Hz Power Line Interference (PLI). We have investigated the performance of eight adaptive algorithms, viz. Least Mean Square (LMS), Least Mean Fourth (LMF), Least Mean Mixed-Norm (LMMN), Sign Regressor Least Mean Square (SRLMS), Sign Error Least Mean Square (SELMS), Sign-Sign Least Mean Square (SSLMS), Sign Regressor Least Mean Fourth (SRLMF), and Sign Regressor Least Mean Mixed-Norm (SRLMMN) in terms of Signal-to-Noise Ratio (SNR) improvement for removing the aforementioned four artifacts from the ECG signal. We employed the LMMN, LMF, LMMN, LMF algorithms in the proposed cascaded 4-stage adaptive noise canceller to remove the respective ECG artifacts as mentioned above. We succeeded in achieving an SNR improvement of 12.7319 dBs. The proposed cascaded 4-stage adaptive noise canceller employing the LMMN, LMF, LMMN, LMF algorithms outperforms those that employ the same algorithm in the four stages. One unique and powerful feature of our proposed cascaded 4-stage adaptive noise canceller is that it employs only those adaptive algorithms in the four stages, which are shown to be effective in removing the respective ECG artifacts as mentioned above. Such a scheme has not been investigated before in the literature.

Speech Enhancement through Implementation of Adaptive Noise Canceller Using FHEDS Adaptive Algorithm

Speech Enhancement through Implementation of Adaptive Noise Canceller Using FHEDS Adaptive Algorithm

Supervised speech separation combined with adaptive beamforming

Microphone arrays are a powerful tool for ambient noise suppression. A multi-channel minimum mean square error (MMSE) solution can be factorized into a minimum variance distortionless response beamformer (MVDR) followed by a single-channel Wiener post-filter. MVDR beamformer, as well as its equivalent form of generalized sidelobe canceller (GSC), often does not provide sufficient noise reduction due to its limited ability to reduce diffuse noise and reverberation. Steering and calibration errors also degrade the performance of both MVDR and GSC beamformers. Post-filter can be realized by any single-channel noise reduction method. A modern and promising approach for single-channel noise reduction is formulated as a supervised speech separation (SSS) in which a supervised learning algorithm, typically a deep neural network (DNN), is trained to learn a mapping from the noisy features to a time-frequency representation of the target of interest. In this paper, we combined SSS and adaptive beamforming approaches. Adaptive beamforming is realized by simplified GSC (S-GSC) whose equivalence with MVDR beamformer is also proved in the paper. In the proposed S-GSC beamformer, the conventional beamformer is replaced by the central microphone signal. Steering towards the target speaker needs no direction of arrival (DOA) estimation. Trained DNN of the SSS module estimates ideal ratio mask (IRM) which is used for adaptation of the blocking matrix, calibration of the microphones, adaptation for the adaptive noise canceller, and the post-filtering. The proposed method was tested on 720 utterances of the TIMIT database used as target speech. The reverberant room was simulated by acoustic impulse responses recorded in the real room. Performance analysis was carried out with PESQ, STOI, and SDR measures. The test results showed that the proposed combined method outperforms the individual SSS and S-GSC methods.

Improving time–frequency sparsity for enhanced audio source separation in degenerate unmixing estimation technique algorithm

In recent years, much research has been focused on separating acoustic sources from their mixtures. Degenerate Unmixing Estimation Technique (DUET) is one of the widely popular methods of Blind Source Separation (BSS) in underdetermined scenarios. DUET is based on a signal recovery sparsity algorithm whose performance is strongly influenced by sparsity in the Time-Frequency (TF) domain. Noises and an several sources in mixtures limit the sparsity resulting in performance degradation in DUET. Here an enhanced strategy has been adopted by combining DUET with adaptive noise cancellation utilising the Dual-Tree Complex Wavelet Transform (DTCWT) as a pre-processor and TF refinement utilising Synchroextracting Transform (SET) as a post-processor. This improves the sparsity of sources and energy concentrations in a TF representation. Results of the signal separation performance evaluation reveal that the proposed algorithm outperforms conventional DUET in signal separation, especially in real-time scenarios.