Adaptive Noise Reduction Algorithm Research Articles

A machine-learning architecture with two strategies for low-speed impact localization of composite laminates

In this paper, a machine-learning architecture with the integration of two strategies including data enhancement and adaptive generation scheme for Impact Localization (IL) are developed to address the aforementioned issues for location identification of impacts on composite laminates. Two main contributions are included in this research: First, response signals collected from low-speed impact experiments under various working conditions are denoised using Adaptive Sparse Noise Reduction Algorithm (ASNRA), which aims at maximizing the preservation of the original signal amplitude, thereby avoiding the underestimation of pulse features during denoising. Then a RIME-optimized Dual-layer Support Vector Regression (RDSVR) method for the real-time update of hyperparameters is implemented in the machine-learning architecture to realize IL. The superior performances of the IL architecture over different IL models are validated throughout the numerical examples in terms of stability and efficiency. Results demonstrate that proposed architecture has the ability to realize the accurate and robust IL of composite laminates.

CNN-based noise reduction for multi-channel speech enhancement system with discrete wavelet transform (DWT) preprocessing.

Speech enhancement algorithms are applied in multiple levels of enhancement to improve the quality of speech signals under noisy environments known as multi-channel speech enhancement (MCSE) systems. Numerous existing algorithms are used to filter noise in speech enhancement systems, which are typically employed as a pre-processor to reduce noise and improve speech quality. They may, however, be limited in performing well under low signal-to-noise ratio (SNR) situations. The speech devices are exposed to all kinds of environmental noises which may go up to a high-level frequency of noises. The objective of this research is to conduct a noise reduction experiment for a multi-channel speech enhancement (MCSE) system in stationary and non-stationary environmental noisy situations with varying speech signal SNR levels. The experiments examined the performance of the existing and the proposed MCSE systems for environmental noises in filtering low to high SNRs environmental noises (-10 dB to 20 dB). The experiments were conducted using the AURORA and LibriSpeech datasets, which consist of different types of environmental noises. The existing MCSE (BAV-MCSE) makes use of beamforming, adaptive noise reduction and voice activity detection algorithms (BAV) to filter the noises from speech signals. The proposed MCSE (DWT-CNN-MCSE) system was developed based on discrete wavelet transform (DWT) preprocessing and convolution neural network (CNN) for denoising the input noisy speech signals to improve the performance accuracy. The performance of the existing BAV-MCSE and the proposed DWT-CNN-MCSE were measured using spectrogram analysis and word recognition rate (WRR). It was identified that the existing BAV-MCSE reported the highest WRR at 93.77% for a high SNR (at 20 dB) and 5.64% on average for a low SNR (at -10 dB) for different noises. The proposed DWT-CNN-MCSE system has proven to perform well at a low SNR with WRR of 70.55% and the highest improvement (64.91% WRR) at -10 dB SNR.

Prediction of Joint Angles Based on Human Lower Limb Surface Electromyography

Wearable exoskeletons can help people with mobility impairments by improving their rehabilitation. As electromyography (EMG) signals occur before movement, they can be used as input signals for the exoskeletons to predict the body’s movement intention. In this paper, the OpenSim software is used to determine the muscle sites to be measured, i.e., rectus femoris, vastus lateralis, semitendinosus, biceps femoris, lateral gastrocnemius, and tibial anterior. The surface electromyography (sEMG) signals and inertial data are collected from the lower limbs while the human body is walking, going upstairs, and going uphill. The sEMG noise is reduced by a wavelet-threshold-based complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) reduction algorithm, and the time-domain features are extracted from the noise-reduced sEMG signals. Knee and hip angles during motion are calculated using quaternions through coordinate transformations. The random forest (RF) regression algorithm optimized by cuckoo search (CS), shortened as CS-RF, is used to establish the prediction model of lower limb joint angles by sEMG signals. Finally, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) are used as evaluation metrics to compare the prediction performance of the RF, support vector machine (SVM), back propagation (BP) neural network, and CS-RF. The evaluation results of CS-RF are superior to other algorithms under the three motion scenarios, with optimal metric values of 1.9167, 1.3893, and 0.9815, respectively.

Adaptive Noise Reduction Algorithm Based on SPP and NMF for Environmental Sound Event Recognition under Low-SNR Conditions

Nonnegative matrix factorization- (NMF-) based noise reduction methods can effectively improve the performance of environmental sound recognition. However, when the environmental sound overlaps highly with the noise, the spectral line loss and noise residue will occur in the low signal-to-noise ratio (SNR) condition. An adaptive noise reduction algorithm was proposed in this paper. First, noisy environmental sound is separated into estimated noise and environmental sound using NMF. Then, the estimated noise is used to calculate sound presence probability (SPP), which is adapted to decrease spectral line loss and achieve accurately estimated noise. Subsequently, the estimated noise combines with noisy environmental sound to obtain the estimated environmental sound. Finally, SPP is applied to reduce residual noise in the estimated environmental sound and reconstruct the environmental sound. The simulation results demonstrate that the proposed algorithm outperforms the traditional algorithms and NMF-based methods in terms of perceptual evaluation of speech quality (PESQ) and global SNR with increase of X% and X%, respectively. Moreover, the proposed method can effectively improve the environmental sound recognition rate. Particularly, the proposed method makes a 16.2% increase of F1-score in car horn recognition under the realistic acoustic condition.

Noise Reduction Based on Adaptive Prediction Fitting Algorithm for a Heterodyne Φ-OTDR System

Aiming to reduce the noise better in distributed optical fiber vibration sensing system, an adaptive predictive filtering algorithm is proposed to improve the overall signal-to-noise ratio (SNR) of vibration detection. A phase-sensitive optical time domain reflectometric ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> -OTDR) system based on heterodyne detection with dual AOMs is designed to reduce the requirement of DAQ high sampling rate, and an adaptive prediction noise reduction method based on normalized least mean square (NLMS) is adopted to reduce the non-stationary noise. Experimental results show that the SNR is increased from 8.14dB to 22.31dB. Compared with moving difference average method, wavelet denoising method and Prewitt edge detection method, the SNR of the adaptive prediction noise reduction algorithm is the highest, which would have potential application in disturbance detection under a strong noise environment.

Effect of adaptive beamforming and noise reduction algorithms on speech intelligibility and noise tolerance in bimodal cochlear implant users

Objective To evaluate the effect of a directional microphone (beamformer) and continuous noise reduction algorithms (CNRs) in bimodally aided cochlear implant (CI) users and to find the optimum hearing aid (HA) and CI settings of the beamformer and CNRs. Methods Speech reception threshold (SRT) and acceptable noise level (ANL) were assessed in twelve adult CI users for unilateral CI, unilateral HA and bimodal listening. To assess the effect of the UltraZoom beamformer and the ClearVoice™ and NoiseBlock CNRs on SRT and ANL, speech was presented from the front in multi-source speech-shaped noise. Results With unilateral CI, application of UltraZoom resulted in a significant improvement of the median SRT and ANL by 3.8 and 4.3 dB, respectively. For bimodal listening with UltraZoom, a significant improvement of 3.5 dB in median SRT was found. There was no significant effect of using the CNRs on speech intelligibility in noise or noise tolerance in either listening condition. Conclusion UltraZoom should be applied on the CI to improve speech intelligibility in face to face conversations with multi-source background noise. However, due to the heterogeneous data, no recommendations for the settings of UltraZoom on the HA or of ClearVoice/NoiseBlock on either device can be given. Trial registration German Clinical Trials Register identifier: DRKS00010807

블록 방법에 의한 적응적 영상 잡음제거 알고리즘

블록 방법에 의한 적응적 영상 잡음제거 알고리즘

GPU Accelerated Image Processing in CCD-Based Neutron Imaging

Image processing is an important step in every imaging path in the scientific community. Especially in neutron imaging, image processing is very important to correct for image artefacts that arise from low light and high noise statistics. Due to the low global availability of neutron sources suitable for imaging, the development of these algorithms is not in the main scope of research work and once established, algorithms are not revisited for a long time and therefore not optimized for high throughput. This work shows the possible speed gain that arises from the usage of heterogeneous computing platforms in image processing along the example of an established adaptive noise reduction algorithm.

Adaptive Noise Reduction Algorithm to Improve R Peak Detection in ECG Measured by Capacitive ECG Sensors.

Electrocardiograms (ECGs) can be conveniently obtained using capacitive ECG sensors. However, motion noise in measured ECGs can degrade R peak detection. To reduce noise, properties of reference signal and ECG measured by the sensors are analyzed and a new method of active noise cancellation (ANC) is proposed in this study. In the proposed algorithm, the original ECG signal at QRS interval is regarded as impulsive noise because the adaptive filter updates its weight as if impulsive noise is added. As the proposed algorithm does not affect impulsive noise, the original signal is not reduced during ANC. Therefore, the proposed algorithm can conserve the power of the original signal within the QRS interval and reduce only the power of noise at other intervals. The proposed algorithm was verified through comparisons with recent research using data from both indoor and outdoor experiments. The proposed algorithm will benefit a noise reduction of noisy biomedical signal measured from sensors.

Detecting region of interest for cadastral images in Taiwan

In the paper, we proposed a coarse-to-fine scheme to automatically detect the regions of interest (ROIs) for digitized cadastral images in Taiwan. To consider some issues such as the skew effect, image quality, and noise in digitized cadastral images, the proposed scheme is composed of four parts: pre-processing, skew correction, noise reduction, and ROI localization. In the pre-processing, each cadastral image is normalized and the prior knowledge is used to find the candidate region of the ROI. To reduce the impact of noise and poor contrast on line detection, an adaptive thresholding is adopted. To decrease the skew effect, the detected horizontal and vertical lines are analyzed to estimate the skew angle. After skew correction, an adaptive noise reduction algorithm is devised to reduce the effect of marginal, artificial, and random noise. The coordinates of the candidate region in the de-skew image with high resolution can be found and then the ROI boundary can be located correctly in the fine detection. Experimental results demonstrate that the proposed scheme can effectively and correctly localize ROIs in digitized cadastral images.

A new two‐microphone Gauss‐Seidel pseudo affine projection algorithm for speech quality enhancement

SummaryThis study addresses the problem of speech quality enhancement by adaptive and nonadaptive filtering algorithms. The well‐known two‐microphone forward blind source separation (TM‐FBSS) structure has been largely studied in the literature. Several two‐microphone algorithms combined with TM‐FBSS have been recently proposed. In this study, we propose 2 contributions: In the first, a new two‐microphone Gauss‐Seidel pseudo affine projection (TM‐GSPAP) algorithm is combined with TM‐FBSS. In the second, we propose to use the new TM‐GSPAP algorithm in speech enhancement. Furthermore, we show the efficiency of the proposed TM‐GSPAP algorithm in speech enhancement when highly noisy observations are available. To validate the good performances of our algorithm, we have evaluated the adaptive filtering properties in computational complexity and convergence speed performance by system mismatch criteria. A fair comparison with adaptive and nonadaptive noise reduction algorithms are also presented. The adaptive algorithms are the well‐known two‐microphone normalized least mean square algorithm, and the recently published two‐microphone pseudo affine projection algorithm. The nonadaptive algorithms are the one‐microphone spectral subtraction and the two‐microphone Wiener filter algorithm. We evalute the quality of the output speech signal in each algorithm by several objective and subjective criteria as the segmental signal‐to‐noise ratio, cepstral distance, perceptual evaluation of speech quality, and the mean opinion score. Finally, we validate the superior performances of the proposed algorithm with physically measured signals.

Sub-band adaptive noise reduction algorithm to improve speech intelligibility

Sub-band adaptive noise reduction algorithm to improve speech intelligibility

ECG SIGNALS NOISE REMOVAL: SELECTION AND OPTIMIZATION OF THE BEST ADAPTIVE FILTERING ALGORITHM BASED ON VARIOUS ALGORITHMS COMPARISON

The electrocardiogram (ECG) is generally used for the diagnosis of cardiovascular diseases. In many of the biomedical applications, it is necessary to remove the noise from ECG recordings. Several adaptive filter structures have been proposed for noise cancellation. Compared to the least mean square (LMS) method, the unbiased and normalized adaptive noise reduction (UNANR) algorithm has better performance, as mentioned in previous investigations. In this paper, we review various kinds of ECG noise reduction algorithms. To provide a detailed and fair comparison, all normalized LMS (NLMS), Block LMS (BLMS), recursive least squares (RLS) and UNANR algorithms are implemented and their performance have been assessed using the same dataset and compared to different state-of-the-art approaches. Then, the performance analysis of all five algorithms is presented and compared in term of mean squared error (MSE), computational complexity and stability. The obtained results revealed that RLS method is much more effective and powerful than other methods in ECG noise cancellation, and even better than UNANR. Then, in order to reach the best performance of the mentioned filter and also, to minimize the output signal error, the optimized parameters of the algorithm were defined and results were investigated. The obtained outcomes show that the best Lambda (λ) occurs between 0.05 and 0.9, so that the convergence rate of the optimized RLS filter is faster than others. It not only decreases the noise, but also the ECG waveform is better conserved. Furthermore, the introduced optimized method with adaptive threshold value would have great potential in biomedical application of signal processing and other fields.

Designing a Planar Antenna for Reading Device of the Capsule Endoscopic Complex

This article describes the design of a planar antenna, which consists of available small-scale antenna elements and can be used in receivers with adaptive noise reduction algorithms, in particular, the receiving antenna for capsule endoscopic system “Landish”. The entire planar antenna simulation results are described, material for common dielectric substrate is chosen. The differences between the characteristics of the radiation field in the far zone, the voltage standing wave ratio and wave resistance depending on the thickness of the substrate is noted. The finite element method was used for numerical calculations.

Power Quality Signal De-noising with Sub band Adaptive Algorithm

A new level-dependent sub band adaptive noise reduction algorithm based on wavelet transform is proposed in order to improve the effect of power quality signal de-noising for power quality monitoring system. This threshold algorithm has two adjustable parameters to adjust the threshold both fine and coarsely, and the optimal parameters are determined by BP neural networks algorithm. Power disturbance data is referred to actual power disturbance data at IEEE open source and applied for test. The test results indicate that the proposed algorithm could denoise the different kind of power disturbances effectively, and the signal noise ratio is improved further with a smaller mean square error.

Noise reduction technologies implemented in head-worn preprocessors for improving cochlear implant performance in reverberant noise fields

The purpose of this study was to investigate whether a multichannel adaptive directional microphone and a modulation-based noise reduction algorithm could enhance cochlear implant performance in reverberant noise fields. A hearing aid was modified to output electrical signals (ePreprocessor) and a cochlear implant speech processor was modified to receive electrical signals (eProcessor). The ePreprocessor was programmed to flat frequency response and linear amplification. Cochlear implant listeners wore the ePreprocessor–eProcessor system in three reverberant noise fields: 1) one noise source with variable locations; 2) three noise sources with variable locations; and 3) eight evenly spaced noise sources from 0° to 360°. Listeners’ speech recognition scores were tested when the ePreprocessor was programmed to omnidirectional microphone (OMNI), omnidirectional microphone plus noise reduction algorithm (OMNI + NR), and adaptive directional microphone plus noise reduction algorithm (ADM + NR). They were also tested with their own cochlear implant speech processor (CI_OMNI) in the three noise fields. Additionally, listeners rated overall sound quality preferences on recordings made in the noise fields. Results indicated that ADM+NR produced the highest speech recognition scores and the most preferable rating in all noise fields. Factors requiring attention in the hearing aid-cochlear implant integration process are discussed.

Adaptive noise reduction algorithms based on statistical hypotheses tests

In many video processing applications, the presence of a random noise is troublesome since most video enhancement functions produce visual artifacts if a priori of the noise is incorrect. The basic difficulty is that the noise and the signal are difficult to be distinguished. It was shown that the noise and image feature detection problem can be converted to statistical hypotheses tests based on the sample correlation in different orientations. In this paper, to further elaborate these hypotheses, we propose parametric, semi- parametric, and nonparametric statistical tests by combining with adaptive median filters. The proposed algorithms provide ways of measuring the degree of noise with respect to the degree of image feature, and the proposed adaptive noise reduction filtering framework provides good performance when the underlying noises are from Gaussian or non-Gaussian distributions. Simulation results for noise reduction show that the Bartlett and the Levene tests perform better regardless of the noise characteristics. Applications of the proposed algorithms can be found in digital TV, camcorders, digital cameras, and DVD players.

Differential microphone array for speech recognition

Speech recognition applications embedded on a PDA are already available on the market. The usual hardware for this kind of system is a single microphone mounted on the PDA, giving good results within quiet environments. Though, the recognition rate falls drastically as the signal to noise ratio decreases. Arrays of microphones are then particularly interesting, allowing the discrimination of useful sounds sources within parasitic ones, thanks to an improved directivity pattern. In speech recognition applications on PDA, a trade-off is to be found on an array small enough to guarantee the ergonomics of the whole system, while operating on a bandwidth covering the frequency range of human voice, from 300 Hz up to 6 kHz and low distortion ratio. Differential arrays are well adapted to fulfill these specifications, since they are known to be robust and allow small dimensions for a high directivity index. The performances could be improved by the addition of adaptive post-filtering and noise reduction algorithms. This work describes the design and the implementation of a recording device dedicated to speech recognition applications on PDA, based on overlapping differential arrays. The assessment of its performances in a noisy environment is carried out and show the system efficiencies.

A Practical Design Flow of Noise Reduction Algorithm for Video Post Processing

This paper presents a practical design flow, including a detailed evaluation methodology, for noise reduction algorithm used specifically in video post processing. Based on this design flow, a novel motion adaptive noise reduction algorithm is created. While integrated into an existing video post processing pipeline, this noise reduction algorithm helps the video pipeline to pass the noise reduction test and detail preserving test in the industry widely used video post processing benchmark, HQV (Hollywood quality video processing for high definition).

Effective compression and noise reduction configurations for hearing protectors

The author proposed to adopt wide dynamic range compression and adaptive multichannel modulation-based noise reduction algorithms to enhance hearing protector performance. Three experiments were conducted to investigate the effects of compression and noise reduction configurations on the amount of noise reduction, speech intelligibility, and overall preferences using existing digital hearing aids. In Experiment 1, sentence materials were recorded in speech spectrum noise and white noise after being processed by eight digital hearing aids. When the hearing aids were set to 3:1 compression, the amount of noise reduction achieved was enhanced or maintained for hearing aids with parallel configurations, but reduced for hearing aids with serial configurations. In Experiments 2 and 3, 16 normal-hearing listeners' speech intelligibility and perceived sound quality were tested when they listened to speech recorded through hearing aids with parallel and serial configurations. Regardless of the configuration, the noise reduction algorithms reduced the noise level and maintained speech intelligibility in white noise. Additionally, the listeners preferred the parallel rather than the serial configuration in 3:1 conditions and the serial configuration in 1:1 rather than 3:1 compression when the noise reduction algorithms were activated. Implications for hearing protector and hearing aid design are discussed.