Adaptive Noise Cancellation Research Articles

Anti-interference photoacoustic microscopy with adaptive noise cancellation and echo recovery for in vivo ocular imaging

Anti-interference photoacoustic microscopy with adaptive noise cancellation and echo recovery for <i>in vivo</i> ocular imaging

Online bone/air-conducted speech fusion in the presence of strong narrowband noise

Online adaptive bone/air-conducted speech fusion is a very promising and attractive technique for real time speech communication in noisy environments. This paper firstly demonstrates that conventional bone/air-conducted speech fusion systems may degrade significantly, despite that only a tiny narrowband noise pollution persists in bone-conducted speech by extensive simulations and experiments. To address this issue, a robust nonlinear system is proposed that successfully enhances the speech contaminated by the harsh narrowband noise. The penetrated narrowband noise is extracted by an adaptive linear prediction filter from an extra reference signal that is capture by a non-contacted bone sensor, and then fed to the parallel adaptive noise cancellers to reconstruct a cleaner error feedback for the speech recovery system. Due to this combination, the proposed system enjoys both attractive robustness against the harsh additive noise and strong real-time capability. Simulations as well as application to a real factory noise are conducted to demonstrate the superior performance of the proposed system.

Sigmoid distance metric-based spline adaptive filters for nonlinear adaptive noise cancellation

This article devises a spline adaptive filter (SAF) algorithm with robustness for nonlinear adaptive noise cancellation (NANC), called the SAF-SDM, which is built on a distance-based metric with a sigmoid kernel. The presented SAF-SDM delivers insignificant coefficient updates when the system is subjected to remarkable outliers, which leads to low steady-state errors. Additionally, we thoroughly analyze the mean behaviour and mean-square performance of the SAF-SDM at the theoretical level and validate the analysis results using nonlinear system identification (NSI) simulations. To fulfill the higher computational efficiency when a longer linear filter order is selected in the SAF, an improved SAF-SDM is developed through a frequency domain (FD) filtering program, called the FDSAF-SDM. Then, we provide an analysis of the calculation complexity of the two presented algorithms and compare them with those of several similar algorithms. Finally, simulation and experiment results demonstrate the excellence of the presented algorithms in NSI and NANC.

Pre-processing the Photoplethysmography Signals for Enhancing the Cardiovascular Diseases Detection for Wrist Pulse Analysis in Nadi Ayurveda

INTRODUCTION: In recent years, Photoplethysmography (PPG) signal has played a vital role in detecting Cardiovascular Diseases (CVDs) in case of wrist pulse analysis emulating the Nadi Ayurveda. The PPG signals acquired from the sensor measurement are severely distorted by various artifacts, which significantly impact the accuracy of disease detection and hamper the disease diagnosis process. OBJECTIVES: Removing the noises is essential before detecting CVDs from the signals and thus, developing a simple and effective noise reduction method for enhancing the PPG signal quality constitutes a challenging research problem, particularly with prominent artifacts. METHODS: This paper designs an effective pre-processing technique that improves denoising methods to enhance the PPG signal quality. The design of pre-processing technique contains two major phases: Primary denoising-based artifact removal and secondary denoising-based Premature Ventricular Contraction (PVC) detection and Power-Line Interference (PLI) noise removal. The primary denoising method involves coarse and fine-grained filtering. The coarse-grained filtering removes the major artifacts, such as Baseline Wander (BLW) and Motion Artifacts (MA), by developing the Two-Stage Adaptive Noise Canceller (TANC) method. The fine-grained filtering process utilizes a detrended filter to filter the refined signal obtained from the TANC method. For the signals filtered from the primary denoising method, the secondary denoising method targets to detect the PVC-induced PPG signals from the decomposed high-frequency signals and removes high-frequency noise, such as PLI from noisy signals, by adopting the Wavelet Transform (WT) method. RESULTS: During the signal reconstruction process in the WT method, the research work reconstructs the denoised PPG signals along with the PVC-induced PPG signals. The experimental results of the noise removal methodology illustrated significant improvements in PPG signal quality. CONCLUSION: The designed pre-processing technique effectively denoises PPG signals, leading to enhanced signal quality which can further aid in accurate disease detection.

Design and analysis of allan variance based adaptive noise canceller for vehicular visible light communication

Design and analysis of allan variance based adaptive noise canceller for vehicular visible light communication

Abstract 3685: Improved detection of minimal residual disease in colorectal cancer patients using adaptive noise cancellation algorithm

Abstract Objective: Existing methods for detecting circulating-tumor DNA (ctDNA) through fixed panels exhibit constrained sensitivity, while customized panels are time-consuming and rely on prerequisite tumor mutation profiles. Here, we aimed to introduce an adaptive noise cancellation approach for efficient Minimal Residuals Disease (MRD) detection in colorectal cancer patients employing a fixed panel under both tumor-informed and tumor-naïve situations. Methods: 108 plasma samples were collected from 52 colorectal cancer patients (11 relapse, 41 non-relapse) at various post-surgical timepoints. Comprehensive mutational profiling of plasma samples was conducted using a fixed MRD panel (SHIELDINGTM ULTRA) that encompasses hotspots in 2365 cancer-related genes. Background errors were removed by an adaptive noise cancellation algorithm relying on DNA fragment profiling, normal pool, and white blood cell background and intra-run plasma sampling. Variants were called both with (tumor-informed) and without (tumor-naïve) the present of tumor tissue. Results: Our longitudinal analysis of 108 plasma samples yielded 90.9% sensitivity, 97.6% specificity and 97.6% negative predictive value in the tumor-informed scenario. The ctDNA positive patients exhibited significantly higher risk than those with negative ctDNA status (Hazard Ratio = 87.2, p = 3.5 × 10^(-5)). For landmark analysis, 33 plasma samples (≤7 days post-operation) were assessed, yielding a remarkable performance of 66.7% sensitivity and 100% specificity. Our approach showed robustness under tumor-naïve scenario, illustrating 90.9% sensitivity, 92.7% specificity and 97.4% NPV for the longitudinal analysis. This corresponded to an increased risk of 49 times in the ctDNA positive patients (HR = 49.0, p = 2.2 × 10^(-4)). Notably, the results of tumor-naïve samples closely aligned with those of tumor-informed samples, showing a concordance of 92.6% and substantial agreement (Cohen’s Kappa = 0.691). Conclusion: Our method stands as an ultra-sensitive tool for identifying colorectal patients at high risk of recurrence. By employing an adaptive noise cancellation algorithm, our fixed panel approach demonstrates superiority over existing fixed panels in accuracy and proves to be a cost-effective alternative to personalized panels. Significantly, it extends potential clinical utilization towards tumor-naïve patients, which is not feasible for personalized panels. Citation Format: Hua Bao, Wanxiangfu Tang, Ningyou Li, Min Wu, Jinfeng Zhang, Baihan Zhu, Xue Wu, Yang Shao. Improved detection of minimal residual disease in colorectal cancer patients using adaptive noise cancellation algorithm [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3685.

Research on sound signal filtering and processing delay based on multiple receivers

With the rapid development of modern communication and audio technology, sound signal processing has become more and more important. This paper deeply explores the potential of multi-receiver audio signal processing technology based on practical application scenarios, and studies the effect of beamforming technology and adaptive noise cancellation algorithm on audio signal quality improvement. Experimental results show that with proper technology selection and framework design, the audio signal processing effect in complex environments can be significantly improved. In addition, this paper also predicts the trend of signal filtering and processing in the future and puts forward suggestions for the application of deep learning in this field, the research of adaptive algorithms, the fusion of multi-sensor information, the optimization of computational efficiency, and the establishment of real scene simulation. Overall, sound signal processing has great potential and opportunities in practical applications, which are worthy of further research and exploration.

A Novel Approach Toward Algorithm Architecture Co-Optimization for the Application of Adaptive Noise Cancellation for Wireless Communication

As FPGA has been in trend for a few decades in prototyping simple to complex DSP systems, some issues are still highlighted in FPGA-based implementations. One issue is the limited resources mounted onboard. Optimization has always remained a choice of developers, either hardware or software-based. Algorithm architecture co-optimization is a domain that incorporates some changes in existing algorithms besides bringing some ways to produce compact architecture. One of the methods in architecture optimization is to use short-word length-based DSP systems that use a sigma-delta modulation (SDM) approach to reduce the actual data word length from multi-bit to single-bit. SDM in the design causes the system to become compact and efficient. This paper produces algorithm architecture co-optimization for the application of adaptive noise cancellers for wireless communication. The algorithm taken is SDM-based Steepest-Descent, and its implementation is compared with the new proposed SDM-based correlation-less design. Both approaches are simulated in MATLAB, and their functional verification is carried out along with comparing some statistical parameters, including SNR, MSE, and PE. Besides, both the designs are translated on Vertex-7 FPGA to verify the less resources consumed by the proposed method. The MATLAB and FPGA-based results indicate that the proposed design may be the best choice for less sensitive applications, like voice or video.

A Review on Higher Order Continuous-Time Sigma-Delta Modulators for ADC Design

Abstract—In this paper, the concept of adaptive filtering has been proposed. It has been shown that signals often undergo variation in the noise environment encountered. Hence it becomes mandatory to design filters based on noise shaping and adaptive noise cancellation. Such filters are called adaptive filters and are used for applications requiring real time noise cancellation such as speech processing, live video broadcasting etc. The paper presents the structure of Sigma Delta or MASH ADCs which employ recursive encoding. SDM ADCs are also used to convert high bit-count, low- frequency digital signals into lower bit-count, higher-frequency digital signals as part of the process to convert digital signals into analog as part of a digital-to-analog converter (DAC). The paper focuses on the various approaches used thus far for the purpose, citing the pros and cons of each approach. Keywords— Adaptive Filter Design, Noise Filtering, Sigma Delta Modulator (SDM), MASH, Signal to Noise Ratio.

A Nonlinear Functional Link Multilayer Perceptron Using Volterra Series as an Adaptive Noise Canceler for the Extraction of Fetal Electrocardiogram.

Uninterrupted monitoring of fetal cardiac health is essential for the timely diagnosis of congenital diseases. The maternal Electrocardiogram (mECG), which has the most significant impact, always tampers with the signals collected from the pregnant woman's abdomen. So, an efficient nonlinear filtering network based on artificial neural network (ANN) is required to eliminate the maternal part from the abdominal Electrocardiogram (aECG) that is traveled from the thoracic of the mother to the abdomen following nonlinear dynamics. In this work, we have presented an adaptive noise canceler (ANC) using 3-layer perceptron architecture where the inputs are expanded by the functional link expansion using the second-order Volterra series, and the weights are updated using backpropagation. The adaptive filter approximates the nonlinear mapping between the thoracic Electrocardiogram (tECG) and the maternal component present in the aECG. Here the thoracic signal is the reference signal, and the abdominal signal is the desired signal to the adaptive filter. The proposed methodology uses the advantages of both multilayer perceptron (MLP) as well as functional link neural network (FLNN) in mapping the nonlinearity and effectively determining the fetal Electrocardiogram (fECG) from the aECG. For the detailed analysis, we have used the real Daisy database, the Non-invasive Fetal ECG database, and the fetal ECG synthetic database from Physionet. The results show that the nonlinear functional link MLP using the Volterra series gives a high-level performance compared to other classical adaptive filtering techniques, as all the evaluation metrics are above 90%.

Practical Considerations for Implementing Adaptive Acoustic Noise Cancellation in Commercial Earbuds

Active noise cancellation has become a prominent feature in contemporary in-ear personal audio devices. However, due to constraints related to component arrangement, power consumption, and manufacturing costs, most commercial products utilize fixed-type controller systems as the basis for their active noise control algorithms. These systems offer robust performance and a straightforward structure, which is achievable with cost-effective digital signal processors. Nonetheless, a major drawback of fixed-type controllers is their inability to adapt to changes in acoustic transfer paths, such as variations in earpiece fitting conditions. Therefore, adaptive-type active noise control systems that employ adaptive digital filters are considered as the alternative. To address the increasing system complexity, design concepts and implementation strategies are discussed with respect to actual hardware limitations. To illustrate these considerations, a case study showcasing the implementation of a filtered-x least mean square-based active noise control algorithm is presented. A commercial evaluation board accommodating a low-cost, fixed-point digital signal processor is used to simplify operation and provide programming access. The earbuds are obtained from a commercial product designed for noise cancellation. This study underscores the importance of addressing hardware constraints when implementing adaptive active noise cancellation, providing valuable insights for real-world applications.

Adaptive noise cancelling for an AUV-mounted passive inverted USBL array

Noise interference of an autonomous underwater vehicle (AUV) is a critical factor affecting the performance of AUV-mounted sonar payloads, especially for passive acoustic systems. Adaptive noise cancellers (ANCs) are used to eliminate noise in many scenarios for signal enhancement. However, a conventional ANC uses a primary sensor to get the data and a secondary sensor to get correlated reference noise input, which adds complexity to the AUV-mounted sonar systems. In this paper, a modified ANC is proposed to improve the performance of an AUV-mounted passive inverted ultra-short baseline (piUSBL) positioning system receiver. Unlike the conventional ANC, the reference input of this ANC is not the noise obtained using a secondary sensor; instead, the transmitted source signal is used as the reference signal, independent of the noise characteristics. The performance of the ANC is thoroughly evaluated using the field navigation data collected from an AUV sea trial. Post-processing results indicate that the ANC can effectively improve the beamforming performance of the piUSBL array, suppressing the beamformed power of the AUV noise by up to 13.8 dB. To our knowledge, this work is the first ANC study for piUSBL, which is of great significance for the further development of piUSBL.

A novel adaptive noise cancellation method based on minimization of error entropy for electrocardiogram denoising

&lt;span&gt;In this paper, we use an adaptive method that conforms to the error entropy criterion in order to eliminate noise from cardiac signals electrocardiogram (ECG). In previous works, the mean squared error (MSE) criterion has been used to adaptive noise cancelation of ECG signals, which only has the ability to minimize the second moment of error. The MSE criterion only works optimally on systems with Gaussian noise and stationary signals, so this is not suitable for ECG signals that are non-stationary and have non-Gaussian noise. In contrast, the use of error entropy-based algorithms like minimum error entropy (MEE) is very useful in ECG noise cancelation. The results of the proposed algorithm indicate a significant advantage in terms of signal-to-noise ratio (SNR) and convergence value compared to the algorithm based on MSE criteria.&lt;/span&gt;

A new particle swarm optimization aided evolutionary digital filter for noise cancellation in early fault diagnosis of rotating machinery

Noise cancellation plays a crucial role in enhancing the signal-to-noise ratio (SNR) of fault characteristics, making it an essential step in fault diagnosis, particularly during the early stages of a fault. Among various adaptive noise cancellation (ANC) methods, evolutionary digital filter (EDF) stands out as an effective approach. EDF employs an adaptive algorithm based on biological evolutionary computation to control its operations. The quality of noise cancellation performance directly relies on the global optimization ability of EDF. However, conventional EDF and its improved versions mainly rely on random search strategies involving cloning and mating, which lack an explicit search direction akin to gradient-based methods. Consequently, substantial computational resources are wasted. To address this problem, an improved algorithm called particle swarm optimization-based evolutionary digital filter (PSO-EDF) is newly proposed. The central concept of this method is to integrate the PSO algorithm into the global optimization process of EDF. By introducing PSO, explicit search directions are provided to individuals adopting the cloning strategy, thus promoting cooperation and information sharing among individuals. The PSO-aided EDF significantly enhances the global optimization capability of ANC filter parameters. Through the proposed PSO-EDF method, the ability to achieve global optimization of ANC filter parameters is substantially improved. This approach mitigates the resource wastage associated with traditional EDF techniques, offering a more efficient and effective solution for noise cancellation in fault diagnosis.

Performance evaluation on a convolutional neural network with adaptive interference cancellation for detecting a buried person's voice in a disaster situation

In a disaster situation, rapid detection of victims is critical to prevent life-threatening accidents. However, it is not easy to detect the target's sound due to noise and interference which are difficult to predict. In addition, it is difficult to collect acoustic data in disaster situations. Therefore, this study proposes a target detection scheme in which untrained noise is added through a binary classification model trained only with a clean target signal and additive white Gaussian noise (AWGN). The feature uses the Mel spectrogram of the signal, and the model adopts a convolutional neural network (CNN). Two acoustic sensors are used in this study to adaptively remove environmental noise and a CNN to detect target signals. The first process trains a CNN using the target signal and AWGN data. In the next step, environmental noise is removed by compensating for the gain difference and delay characteristics between the two acoustic sensors through adaptive filtering. The input signal passes through preprocessing to eliminate noise through an adaptive filter. This signal is converted into a Mel spectrogram, which is then detected through a CNN model. The simulation results show that the model improves from 50% to 99% in detection rate at SNR 0 dB. In a catastrophe, there are many sounds that are difficult to discern. However, the model cannot learn from noise. Therefore, it shows that the target detection method using adaptive filter-based noise cancellation is effective in disaster situations.

Real-time 14N NQR-based sodium nitrite analysis in a noisy field

Noise and Radio-frequency interference or RFI causes a significant restriction on the Free induction Decay or FID signal detection of the Nuclear Quadrupole Resonance procedure. Therefore, using this method in non-isolated environments such as industry and ports requires extraordinary measures. For this purpose, noise reduction algorithms and increasing signal-to-noise-and-interference ratio or SNIR have been used. In this research, sodium nitrite has been used as a sample and algorithms have been tested in a non-isolated environment. The resonant frequencies for the 150 g of test sample were measured at 303 K at about 1 MHz and 3.4 MHz. The main novelty in this study was, (1) using two types of antennas in the receiver to improve adaptive noise and interference cancellation, (2) using a separate helical antenna in the transmitter to eliminate the duplexer, (3) estimating the noise before sending the pulse to calculate the weighting factors and reduce the noise by adaptive noise cancellation, (3) reject the interference by blanking algorithm, (4) pulse integration in the frequency domain to increase the SNR, and (5) increasing the detection speed by new pulse integration technique. By interference rejection and noise cancellation, the SNIR is improved to 9.24 dB at 1 MHz and to 7.28 dB at 3.4 MHz, and by pulse integration 44.8 dB FID signal amplification is achieved, and the FID signals are detected at 1.057 MHz and 3.402 MHz at room temperature.

Internal combustion engine gearbox bearing fault prediction using J48 and random forest classifier

&lt;span lang="EN-US"&gt;Defective bearings in four-stroke engines can compromise performance and efficiency. Early detection of bearing difficulties in 4-stroke engines is critical. Four-stroke gasoline engines that vibrate or make noise can be used to diagnose issues. Using time, frequency, and time-frequency domain approaches, the vibrational features of healthy and diseased tissues are examined. Problems are only detectable by vibration or sound. The fault is identified through statistical analysis of seismic and audio data using frequency and time-frequency analysis. Vibration must be minimized prior to examination. Adaptive noise cancellation removes unwanted noise from recorded vibration signals, boosting the signal-to-noise ratio (SNR). In the first of the experiment's three phases, vibrational data are collected. To reduce noise and boost SNR, adaptive noise cancellation (ANC) is applied to vibration data from the first stage. In the second stage, ANC-filtered vibration data is subjected to three studies to detect bearing failure using J48 and random forest classifiers for online, real-time monitoring. In this experiment, one healthy and two faulty bearings are used. According to a current study, the internet of things (IoT) is a promising alternative for online monitoring of remote body health.&lt;/span&gt;

Speech enhancement using dual microphone adaptive noise canceller

Subject of research. The subject of the study is the issue of rational placement of microphones of the main and reference channels of a two-channel adaptive noise canceller (ANC) for speech enhancement in presence of acoustic interference from a point source. An increase of the signal-to-noise ratio (SNR) at the output of the ANC relative to the input value of the main channel was chosen as a criterion for rational placement. The limitations of the model of direct propagation of signals in reverberation conditions are considered. Methods. The estimation of the SNR at the inputs of the main and reference channels is obtained on the basis of a geometric model of point sources of signal and interference and on the analytical expressions for transfer functions between signal and interference sources and microphones of the main and reference channels for a planar model of signal propagation in a free field. The main results. The conditions for microphones placement of dual channel ANC, increasing SNR, are determined. The conditions for reference channel microphone placement at a given position of main channel microphone are determined. Limitations of ANC efficiency under reverberation conditions are investigated. It is shown that the microphone of the reference channel should be located near the interference source. For large rooms, the use of dual-microphone arrays is proposed and possible signal processing algorithms (conditions of their application) are considered. To use a two-element microphone array, additional information about the position of the target source is required. Practical significance. The obtained results can be useful for distant speech acquisition in the presence of acoustic interference.

Beamforming-based algorithms for recovering information from fetal electrocardiographic sensors

We deal with the extraction of the fetal electrocardiography (ECG) signal from the raw ECG signals of the mother by the beamforming- based algorithms. The foetal ECG sensors bring out signals containing information from the pregnant mother and the infant. Detailed and separate signals are already provided by the foetal ECG instruments; but for some specific studies related to the infant conditions, it is necessary to improve the quality of the signal with a dedicated processing. In this paper, four techniques, with some enhancements, are proposed to perform the processing; we have applied the following techniques: Least Mean Square (LMS) with adaptive noise cancellation technique, Discrete Wavelet Transform (DWT)-based technique, Empirical Wavelet Transform (EWT) technique, and Multiple Signal Classification (MUSIC). The LMS and the MUSIC pertain to beamforming approach. The techniques were used to decompose and identify the different elements constituting the source signal (mother's signal) and noise cancellation by Multivariate Empirical Mode Decomposition (MEMD) technique. The signal was adaptively decomposed by LMS, DWT and MUSIC according to optimised parameters to extract some hidden components of the source signal, such as the foetal features, QRS, heartbeat etc. The results have showed that LMS, with enhancements, is more effective in identifying and removing useless noise. The techniques were applied to the ECG signal of a 30-year-old healthy pregnant woman, which allowed to verify their applicability. The present research leads to the below main contributions among others: separation of the ECG signal of the foetus from the mother, highlighting the functional state of the foetal heart rhythm (heart rate and heartbeat,) and this can show us if the foetal ECG has malfunctions.

Design and Validation of a Low-Cost Mobile EEG-Based Brain-Computer Interface.

Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user's hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal-to-noise ratio (SNR) and common-mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device's use at both the clinic and at home. Significance: The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications.