Adaptive Noise Reduction Research Articles

Evaluation of Potential Benefits and Limitations of Noise-Management Technologies for Children with Hearing Aids.

Children with hearing loss frequently experience difficulty understanding speech in the presence of noise. Although remote microphone systems are likely to be the most effective solution to improve speech recognition in noise, the focus of this study centers on the evaluation of hearing aid noise management technologies including directional microphones, adaptive noise reduction (ANR), and frequency-gain shaping. These technologies can improve children's speech recognition, listening comfort, and/or sound quality in noise. However, individual contributions of these technologies as well as the effect of hearing aid microphone mode on localization abilities in children is unknown. The objectives of this study were to (1) compare children's speech recognition and subjective perceptions across five hearing aid noise management technology conditions and (2) compare localization abilities across three hearing aid microphone modes. A single-group, repeated measures design was used to evaluate performance differences and subjective ratings. Fourteen children with mild to moderately severe hearing loss. Children's sentence recognition, listening comfort, sound quality, and localization were assessed in a room with an eight-loudspeaker array. The use of adaptive directional microphone technology improves children's speech recognition in noise when the signal of interest arrives from the front and is spatially separated from the competing noise. In contrast, the use of adaptive directional microphone technology may result in a decrease in speech recognition in noise when the signal of interest arrives from behind. The use of a microphone mode that mimics the natural directivity of the unaided auricle provides a slight improvement in speech recognition in noise compared with omnidirectional use with limited decrement in speech recognition in noise when the signal of interest arrives from behind. The use of ANR and frequency-gain shaping provide no change in children's speech recognition in noise. The use of adaptive directional microphone technology, ANR, and frequency-gain shaping improve children's listening comfort, perceived ability to understand speech in noise, and overall listening experience. Children prefer to use each of these noise management technologies regardless of whether the signal of interest arrives from the front or from behind. The use of adaptive directional microphone technology does not result in a decrease in children's localization abilities when compared with the omnidirectional condition. The best localization performance occurred with use of the microphone mode that mimicked the directivity of the unaided auricle.

Improved MEMS Magnetometer Adaptive Filter Noise Reduction and Compensation Method

In the actual integrated navigation process of the MEMS three-axis magnetometer, excessive measurement noise and external magnetic field interference often cause the magnetometer heading to be inaccurate, thereby reducing the overall performance of the integrated navigation system. In order to overcome the above influence, this paper proposes an adaptive real-time filtering noise reduction and comprehensive error interference compensation method. We have completed the construction of the measurement noise model and parameter update by analyzing the measurement noise characteristics of the MEMS three-axis magnetometer. We designed an adaptive filtering algorithm with multiple fading memory factors to perform offline noise reduction on the output data of the magnetometer, and use the noise-reduced data for comprehensive compensation of magnetic field interference, which has a good effect. The experimental results show that the proposed method has better filtering and noise reduction ability and higher compensation accuracy in offline state, can effectively reduce noise and compensate the magnetometer, and improve the accuracy of the magnetometer’s heading calculation.

RLS Adaptive Real-Time Noise Reduction Technology for Fiber Optic Gyroscope Based on FPGA

RLS Adaptive Real-Time Noise Reduction Technology for Fiber Optic Gyroscope Based on FPGA

Defocused image restoration method based on micro-nano scale

An image adaptive noise reduction enhancement algorithm based on NSCT is proposed to perform image restoration preprocessing on the defocused image obtained under the microscope. Defocused images acquired under micro-nano scale optical microscopy, usually with inconspicuous details, edges and contours, affect the accuracy of subsequent observation tasks. Due to its multi-scale and multi-directionality, the NSCT transform has superior transform functions and can obtain more textures and edges of images. Combined with the characteristics of micro-nanoscale optical defocus images, the NSCT inverse transform is performed on all sub-bands to reconstruct the image. Finally, the experimental results of the standard 500nm scale grid, conductive probe and triangular probe show that the proposed algorithm has a better image enhancement effect and significantly improves the quality of out-of-focus images.

Multi-target vital signs detection using frequency-modulated continuous wave radar

Respiration and heartbeats rates are important physiological assessment indicators that provide valid prior-knowledge for the diagnosis of numerous diseases. However, most of the current research focuses on the vital signs measurement of single target, and multi-target vital signs detection has not received much attention. In this paper, we use frequency-modulated continuous wave (FMCW) radar to measure the vital signs signals of multi-target. First, we apply the three-dimensional fast Fourier transform (3D-FFT) method to separate multiple targets and get their distance and azimuth information. Subsequently, the linear constrained minimum variance-based adaptive beamforming (LCMV-ADBF) technique is proposed to form a spatially distributed beams on the targets of interest directions. Finally, a compressive sensing based on orthogonal matching pursuit (CS-OMP) method and rigrsure adaptive soft threshold noise reduction based on discrete wavelet transform (RA-DWT) method are present to extract the respiratory and heartbeat signals. We perform tests in a real experimental environment and compare the proposed method with reference devices. The results show that the degrees of agreement for respiratory and heartbeat are 89% and 87%, respectively, for two human targets. The level of agreement for respiratory and heartbeat is 87% and 85%, respectively, for three human targets, proving the effectiveness of the proposed method.

A Hybrid Intelligent Fault Diagnosis Strategy for Chemical Processes Based on Penalty Iterative Optimization

Process fault is one of the main reasons that a system may appear unreliable, and it affects the safety of a system. The existence of different degrees of noise in the industry also makes it difficult to extract the effective features of the data for the fault diagnosis method based on deep learning. In order to solve the above problems, this paper improves the deep belief network (DBN) and iterates the optimal penalty term by introducing a penalty factor, avoiding the local optimal situation of a DBN and improving the accuracy of fault diagnosis in order to minimize the impact of noise while improving fault diagnosis and process safety. Using the adaptive noise reduction capability of an adaptive lifting wavelet (ALW), a practical chemical process fault diagnosis model (ALW-DBN) is finally proposed. Then, according to the Tennessee–Eastman (TE) benchmark test process, the ALW-DBN model is compared with other methods, showing that the fault diagnosis performance of the enhanced DBN combined with adaptive wavelet denoising has been significantly improved. In addition, the ALW-DBN shows better performance under the influence of different noise levels in the acid gas absorption process, which proves its high adaptability to different noise levels.

Sparse grid-based adaptive noise reduction strategy for particle-in-cell schemes

We propose a sparse grid-based adaptive noise reduction strategy for electrostatic particle-in-cell (PIC) simulations. By projecting the charge density onto sparse grids we reduce the high-frequency particle noise. Thus, we exploit the ability of sparse grids to act as a multidimensional low-pass filter in our approach. Thanks to the truncated combination technique [1–3], we can reduce the larger grid-based error of the standard sparse grid approach for non-aligned and non-smooth functions. The truncated approach also provides a natural framework for minimizing the sum of grid-based and particle-based errors in the charge density. We show that our approach is, in fact, a filtering perspective for the noise reduction obtained with the sparse PIC schemes first introduced in [4]. This enables us to propose a heuristic based on the formal error analysis in [4] for selecting the optimal truncation parameter that minimizes the total error in charge density at each time step. Hence, unlike the physical and Fourier domain filters typically used in PIC codes for noise reduction, our approach automatically adapts to the mesh size, number of particles per cell, smoothness of the density profile and the initial sampling technique. It can also be easily integrated into high performance large-scale PIC code bases, because we only use sparse grids for filtering the charge density. All other operations remain on the regular grid, as in typical PIC codes. We demonstrate the efficiency and performance of our approach with two test cases: the diocotron instability in two dimensions and the three-dimensional electron dynamics in a Penning trap. Our run-time performance studies indicate that our approach can provide significant speedup and memory reduction to PIC simulations for achieving comparable accuracy in the charge density.

Методы и алгоритмы шумоочистки звука в реальном времени

The voice cleaning methods and algorithms play a key role both in preprocessing speech for further analysis and recognition, and in improving the quality of communication between users of information networks. The real-time streaming noise cleaning methods are the most important and complex area. The ability to process streaming data without delays imposes a number of significant restrictions on the algorithm: it cannot be iterative with a previously unknown number of iterations, and cannot explicitly use the data before or after the current block being processed. In the work, a modern adaptive noise reduction method for speech that can work with minimal signal transmission delays has been proposed. A large-scale study of existing approaches has been conducted, with special attention paid to two groups of algorithms: noise detection algorithms and noise suppression algorithms. Based on them the developed algorithm meeting the specified requirements has been built and analyzed. A set of audio data of Russian speech with various noises superimposed on it has been created. The testing of the algorithm has been made and its comparison with existing actual noise cleaning methods has been performed. The proposed adaptive method of noise cleaning without using specialized apparatus means and subsidiary information is able to operate in the real time conditions. The testing of the developed algorithm using the metrics of segment NC and PESQ have shown the high efficiency of the development and its superiority to common noise cleaning implementations Speex and WebRTC with respect to the noise cleaning quality and operation speed.

Effect of Noise Adaptive Wavelet Filter on Diagnostic Performance in Stroke Perfusion

Background: Computed tomography perfusion (CTP) images include more noise than routine clinic computed tomography (CT) images. Singular value decomposition based deconvolution algorithms are widely used for obtaining several functional perfusion maps. Recently block circulant singular value decomposition algorithms become popular for its superior property of immunity to contrast bolus lag. It is well known from literature that these algorithms are very sensitive to noise. There are a lot of examples of noise reduction filters in the literature as well as commercial ones. Functional maps which help physicians in the diagnostic process can be obtained with better image quality by de-noising CTP images with adaptive noise reduction filters. Objective: In this study, the effect of a noise adaptive wavelet filtering method on diagnostic performance on CTP stroke patient images is investigated. Method: Images of acute stroke patients were de-noised by this method and their diagnostic value were evaluated by visual means, peak signal-to-noise ratio and time intensity profile metrics. An observer evaluation study was carried out in order to validate quantitative image quality metrics. The results are compared with Gaussian and a bilateral filter based filtering method called TIPS (Time Intensity Profile Similarity) on same images sets to benchmark proposed method. Results: The diagnostic value of the images obtained from noise adaptive wavelet filtering method were better than Gaussian filter method and were compatible with a wellknown time intensity profile similarity bilateral filter method. Diagnostic performance of the both observers were improved compared to both Gaussian and TIPS methods. Conclusion: The noise adaptive wavelet filter method succeeded to reduce noise while preserving details contained in the contrast bolus. Its final effect on the timeintensity profiles and generated perfusion maps are compatible with the literature and showed improvements on diagnostic performance on specificity and overall accuracy when compared to other methods.

Free-size accelerated Kuwahara filter

Kuwahara filter is a smoothing filter used in image processing for adaptive noise reduction that has the ability to preserve object edges. Applications for this filter exist in fields such as medical imaging and artistic imaging. However, it has a very high computational cost, especially when filter size is large. In this paper, we propose an efficient algorithm to accelerate this filter regardless of filter size. Our method uses Summed-area Table (SAT) to gain fast computation of mean and variance values in Kuwahara filter. After that, three acceleration methods, namely caching mean and variance values, memory access optimization and flexible-format SATs are proposed to optimize the SAT-based Kuwahara filter. The experiments show that our method achieves the lowest possible big-O time complexity and performs at around the same level regardless of filter size, while producing the exact same output image as the original method. The achieved speedup ratio grows quadratically with the filter size, ranging from 10x to 1000x and even more. As a result, our optimized filter can run at a high frame rate even when the filter is large.

Detecting Adversarial Image Examples in Deep Neural Networks with Adaptive Noise Reduction

Recently, many studies have demonstrated deep neural network (DNN) classifiers can be fooled by the adversarial example, which is crafted via introducing some perturbations into an original sample. Accordingly, some powerful defense techniques were proposed. However, existing defense techniques often require modifying the target model or depend on the prior knowledge of attacks. In this paper, we propose a straightforward method for detecting adversarial image examples, which can be directly deployed into unmodified off-the-shelf DNN models. We consider the perturbation to images as a kind of noise and introduce two classic image processing techniques, scalar quantization and smoothing spatial filter, to reduce its effect. The image entropy is employed as a metric to implement an adaptive noise reduction for different kinds of images. Consequently, the adversarial example can be effectively detected by comparing the classification results of a given sample and its denoised version, without referring to any prior knowledge of attacks. More than 20,000 adversarial examples against some state-of-the-art DNN models are used to evaluate the proposed method, which are crafted with different attack techniques. The experiments show that our detection method can achieve a high overall F1 score of 96.39% and certainly raises the bar for defense-aware attacks.

An Adaptive Noise Reduction Method Based on Improved Dislocation Superposition Method for Abnormal Noise Fault Component of Automotive Engine

The acoustic signal generated by mechanical motion contains the information of its motion state, but when the signal‐to‐noise ratio (SNR) is low, the accuracy of real‐time monitoring mechanical motion state by the acoustic signal is low. This study proposes an adaptive noise reduction method based on the dislocation superposition method (DSM), which can realize the adaptive noise reduction and the extraction of fault a component from the automobile engine abnormal noise signal of low SNR. Firstly, the wavelet coefficients of engine abnormal noise signal are obtained by continuous wavelet transform (CWT), and the fault feature points of the abnormal noise signal in each period are extracted by setting hard threshold function, window function, and feature points extraction algorithm. Then, the signal segments containing fault components are obtained by using the position of feature points to extend the length of the fault component forward and backward, respectively, and Pearson’s correlation is calculated by traversal to determine the starting superposition point of each signal segment containing fault components. Finally, the signal segments of the odd group and even group are selected for superposition calculation. When the superposition stop condition is not satisfied, the number of superpositions increased until the stop condition is satisfied, and the superposition signal can be used as a fault component. The experimental results show that, compared with the improved DSM, this method has a good effect on the noise reduction and extraction of fault components of automobile engine cylinder knocking fault, and the effectiveness of this method is verified. This method is used to reduce the noise and extract the fault components of automobile engine cylinder missing fault and knock fault, and good results are obtained.

Adaptive noise reduction for dual-energy x-ray imaging based on spatial variations in beam attenuation

Purpose. The main goal of this work is to improve the previously proposed patient-specific pixel-based dual-energy (PP-DE) algorithm by developing an adaptive anti-correlated noise reduction (ACNR) method, resulting in reduced image noise. Methods. Theoretical models of contrast-to-noise (CNR) and signal-to-noise (SNR) ratio were developed as functions of weighting factors for DE bone ω Bn or soft tissue ω ST cancellation. These analytical expressions describe CNR and SNR properties of dual-energy (DE) images, obtained with both simple log subtraction (SLS) and ACNR algorithms, and allow for a direct comparison between experimental and theoretical results. The theoretical models demonstrate the importance of ACNR weighting factor (ω A ) optimization leading to the maximization of the SNR of the final image. A step phantom was constructed, which consisted of overlapping slabs of solid water (0-30 cm) and bone-mimicking material (0-6 cm), resulting in a total of 7 × 7 regions. High-energy (HE) and low-energy (LE) images were acquired at 140 kVp and 60 kVp with a clinical ExacTrac imaging system. The CNR and SNR were obtained for the DE images as functions of ω Bn,ST and noise reduction weighting factor ω A for different combinations of thicknesses. Weighting factors for bone cancellation were optimized for each region of interest (ROI) by finding zeros of CNR function for DE images between soft tissue only and soft tissue plus bone regions (and vice versa for soft tissue cancellation). The weighting factor for the ACNR algorithm ω A was then optimized by maximizing the SNR function for each ROI. HE and LE images for an anthropomorphic Rando phantom were obtained with the same acquisition parameters as for the step phantom. DE images for bone only and soft tissue only were obtained with three algorithms: SLS and PP-DE with conventional ACNR (uniform ω A ), and PP-DE with adaptive ACNR (region-varying ω A ). Weighting factor maps for PP-DE and adaptive ACNR methods were obtained for Rando phantom geometry (which was determined from its CT scans) by interpolation (or extrapolation) of weighting factors for the step phantom. CNR values were calculated for different regions. Results. The CNR and SNR characteristics as functions of material cancellation and noise reduction weighting factors were obtained from theoretical models and experimental data from the step phantom. This showed a good qualitative validation of the models. For the ANCR algorithm, both the theory and experiment demonstrated that the material cancellation weighting factors (ω Bn,ST ) can be optimized independently of the noise cancellation weighting factors (ω A ), which can be optimized by maximizing SNR. For each ROI (with different overlapping bone and soft tissue thicknesses) the weighting factors ω Bn,ST were determined as well as corresponding optimal weighting factors ω A for noise reduction. For the Rando phantom, CNR values for regions representing different anatomical structures (ribs, spine, and tumor) were evaluated. It was shown that the proposed adaptive ACNR further improves image quality, compared to the conventional ACNR algorithm. The improvement is maximized for regions with bones (ribs or spine), where the largest attenuation is observed. Conclusion. The ACNR weighting factors are dependent on the material thicknesses due to varying beam attenuation leading to different levels of quantum noise. This was shown with the derived theoretical expressions of the CNR and SNR functions and was validated by experimental data. The adaptive ANCR DE algorithm was developed, which allows for an increase in image quality by spatially varying weighting factors for noise reduction. This algorithm complements the previously developed PP-DE algorithm to obtain better quality DE images.

Weak feature extraction for early wear of connecting rod bearings under transient conditions

As we all know, it is difficult to extract weak feature for early wear of connecting rod bearings under transient conditions. In order to solve the problem, a method of extracting wear features for connecting rod bearings based on variational modal decomposition (VMD) adaptive noise reduction and computational order tracking (COT) was proposed. Firstly, the vibration signals of internal combustion engine block under transient operating conditions were collected, and the signals were reordered to satisfy the order tracking condition. Then, interpolation and fitting techniques were used to map the signal from the time domain space to the angular domain space. Next, the VMD was used to decompose the angular domain signal into multiple modal components, and the autocorrelation function (ACF) was used to denoise the modal components adaptively. Finally, the signal was reconstructed to conduct COT analysis, and the wear features of connecting rod bearing were extracted by average COT spectrum. The simulation analysis and the simulation experiment of the connecting rod bearing fault show that the proposed method is effective, and the weak features for early wear of connecting rod bearing of internal combustion engine are extracted.

Extracting weak multi-frequency signal in heavy colored noise

The extraction of weak multi-frequency signal in the background of heavy colored noise is studied. For the multi-frequency signal which cannot be directly decomposed by ensemble empirical mode decomposition (EEMD), further processing is made in the paper. Based on the normalized autocorrelation analysis and EEMD, a new method of weak multi-frequency signal detection is proposed in the article. Firstly, the adaptive noise reduction of multi-frequency signal with strong colored noise is realized by the normalized autocorrelation analysis. Then, the denoised multi-frequency signal is decomposed by the EEMD method. A series of monochromatic intrinsic mode functions are obtained to representing the characteristics of multi-frequency signal. Finally, two mechanical fault experiments are designed to verify the applicability of the proposed method in the fault diagnosis. The vibration signals with bearing pedestal bolt looseness fault, bearing outer raceway and rolling element compound fault were collected, respectively. The experimental results show that the weak multi-frequency fault features in the vibration signals with heavy colored noise are effectively extracted by the new method. The proposed method has a good application prospect in the field of signal processing.

Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis

The feature extraction of composite fault of gearbox in mining machinery has always been a difficulty in the field of fault diagnosis. Especially in strong background noise, the frequency of each fault feature is different, so an adaptive time-frequency analysis method is urgently needed to extract different types of faults. Considering that the signal after complementary ensemble empirical mode decomposition (CEEMD) contains a lot of pseudo components, which further leads to misdiagnosis. The article proposes a new method for actively removing noise components. Firstly, the best scale factor of multi-scale sample entropy (MSE) is determined by signals with different signal to noise ratios (SNRs); secondly, the minimum value of a large number of random noise MSE is extracted and used as the threshold of CEEMD; then, the effective Intrinsic mode functions(IMFs) component is reconstructed, and the reconstructed signal is CEEMD decomposed again; finally, after multiple iterations, the MSE values of the component signal that are less than the threshold are obtained, and the iteration is terminated. The proposed method is applied to the composite fault simulation signal and mining machinery vibration signal, and the composite fault feature is accurately extracted.

CNN based Sound Event Detection Method using NMF Preprocessing in Background Noise Environment

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

A Hybrid Optimization Method OWGWA for EEG/ERP Adaptive Noise Canceller With Controlled Search Space

In this paper, a system for filtering event-related potentials/electroencephalograph is exhibited by adaptive noise canceller through an optimization algorithm, oppositional hybrid whale-grey wolf optimization algorithm (OWGWA). The OWGWA can choose the control parameters of the grey wolf algorithm utilizing whale parameters. To balance out the randomness of optimization strategies another methodology is implemented called controlled search space. Adaptive filter's noise reduction capability has been tested through adding adaptive white Gaussian noise over contaminated EEG signals at different noise levels. The performance of the proposed OWGWA-CSS algorithm is evaluated by signal to noise ratio in dB, mean value, and the relationship between resultant and input ERP. In this work, ANCs are also implemented by utilizing other optimization techniques. In average cases of noisy environment, comparative analysis shows that the proposed OWGWA-CSS technique provides higher SNR value, significantly lower mean and higher correlation as compared to other techniques.

Remote Monitoring of Human Vital Signs Based on 77-GHz mm-Wave FMCW Radar.

In recent years, non-contact radar detection technology has been able to achieve long-term and long-range detection for the breathing and heartbeat signals. Compared with contact-based detection methods, it brings a more comfortable and a faster experience to the human body, and it has gradually received attention in the field of radar sensing. Therefore, this paper extends the application of millimeter-wave radar to the field of health care. The millimeter-wave radar first transmits the frequency-modulated continuous wave (FMCW) and collects the echo signals of the human body. Then, the phase information of the intermediate frequency (IF) signals including the breathing and heartbeat signals are extracted, and the Direct Current (DC) offset of the phase information is corrected using the circle center dynamic tracking algorithm. The extended differential and cross-multiply (DACM) is further applied for phase unwrapping. We propose two algorithms, namely the compressive sensing based on orthogonal matching pursuit (CS-OMP) algorithm and rigrsure adaptive soft threshold noise reduction based on discrete wavelet transform (RA-DWT) algorithm, to separate and reconstruct the breathing and heartbeat signals. Then, a frequency-domain fast Fourier transform and a time-domain autocorrelation estimation algorithm are proposed to calculate the respiratory and heartbeat rates. The proposed algorithms are compared with the contact-based detection ones. The results demonstrate that the proposed algorithms effectively suppress the noise and harmonic interference, and the accuracies of the proposed algorithms for both respiratory rate and heartbeat rate reach about 93%.

Audiological outcome measures with the BONEBRIDGE transcutaneous bone conduction hearing implant: impact of noise, reverberation and signal processing features

Objective: To assess the performance of an active transcutaneous implantable-bone conduction device (TI-BCD), and to evaluate the benefit of device digital signal processing (DSP) features in challenging listening environments.Design: Participants were tested at 1- and 3-month post-activation of the TI-BCD. At each session, aided and unaided phoneme perception was assessed using the Ling-6 test. Speech reception thresholds (SRTs) and quality ratings of speech and music samples were collected in noisy and reverberant environments, with and without the DSP features. Self-assessment of the device performance was obtained using the Abbreviated Profile of Hearing Aid Benefit (APHAB) questionnaire.Study sample: Six adults with conductive or mixed hearing loss.Results: Average SRTs were 2.9 and 12.3 dB in low and high reverberation environments, respectively, which improved to −1.7 and 8.7 dB, respectively with the DSP features. In addition, speech quality ratings improved by 23 points with the DSP features when averaged across all environmental conditions. Improvement scores on APHAB scales revealed a statistically significant aided benefit.Conclusions: Noise and reverberation significantly impacted speech recognition performance and perceived sound quality. DSP features (directional microphone processing and adaptive noise reduction) significantly enhanced subjects’ performance in these challenging listening environments.