Adaptive Noise Cancellation Research Articles

Noise Cancellation Employing Adaptive Digital Filters for Mobile Applications

The persistent improvement of the hybrid adaptive algorithms and the swift growth of signal processing chip enhanced the performance of signal processing technique exalted mobile telecommunication systems. The proposed Artificial Neural Network Hybrid Back Propagation Adaptive Algorithm (ANNHBPAA) for mobile applications exploits relationship among the pure speech signal and noise corrupted signal in order to estimate of the noise. An adaptive linear system responds for changes in its environment as it is operating. Linear networks are gets adjusted at each time step based on new input and target vectors can find weights and biases that minimize the networks sum squared error for recent input and target vectors. Networks of this kind are quite oftenly used for error cancellation, speech signal processing and control systems. Noise in an audio signal has become major problem and hence mobile communication systems are demanding noise-free signal. In order to achieve noise-free signal various research communities have provided significant techniques. Adaptive noise cancellation (ANC) is a kind of technique which helps in estimation of un-wanted signal and removes them from corrupted signal. This paper introduces an Adaptive Filter Based Noise Cancellation System (AFNCS) that incorporates a hybrid back propagation learning for the adaptive noise cancellation in mobile applications. An extensive study has been made to explore the effects of different parameters, such as number of samples, number of filter coefficients, step size and noise level at the input on the performance of the adaptive noise cancelling system. The proposed hybrid algorithm consists all the significant features of Gradient Adaptive Lattice (GAL) and Least Mean Square (LMS) algorithms. The performance analysis of the method is performed by considering convergence complexity and bit error rate (BER) parameters along with performance analyzed with varying some parameters such as number of filter coefficients, step size, number of samples and input noise level. The outcomes suggest the errors are reduced significantly when the numbers of epochs are increased. Also incorporation of less hidden layers resulted in negligible computational delay along with effective utilization of memory. All the results have been obtained using computer simulations built on MATLAB platform

Speaker recognition based on pre-processing approaches

This paper presents two pre-processing methods that can be implemented for noise reduction in speaker recognition systems. These methods are adaptive noise canceller (ANC) and Savitzky-Golay (SG) filter. Also, discrete cosine transform (DCT), discrete wavelet transform (DWT) and discrete sine transform (DST) are considered for consistent feature extraction from noisy speech signals. A neural network with only one hidden layer is used as a classifier. The performances of the proposed noise reduction methods are compared with those of a hybrid method that comprises empirical mode decomposition (EMD) and spectral subtraction and also with spectral subtraction method only. Recognition rate is taken as a performance metric to evaluate the behavior of the system with these enhancement strategies. Simulation results prove that the DCT is the optimum transform with the suggested methods, while the DWT is the best one with the hybrid method and the spectral subtraction method.

Low Energy Less Area Less Delay Fixed Point LMS Method for Adaptive Noise Cancellation Filter

Present paper is about the high speed low complexity implementation derived by its architecture using least mean square (LMS) adaptive filtering. Here straight form LMS adaptive filter has almost the similar critical path as it is a reverse from of the counter path hoiver it has a fast coverage and also a loir register complication. Here critical path evaluation tells that no pipelining is necessary for implementation of straight form LMS adaptive filtering in most of the practical cases requires a realized extremely small adaptive delay and very high sampling rate. Here based on these finding LMS adaptive filtering is divided into 3 structural proposal designs. a) There is no adaption delay b) Only one adaption delay c) Only two adaption delay. Here first one includes least area and least energy per sample (EPS).

Harmonic detection method based on adaptive noise cancellation and its application in photovoltaic - active power filter system

The adaptive harmonic detection method based on the adaptive noise cancellation principle has the advantages of strong adaptability and good robustness. However, when the step size of the algorithm is selected, there is a conflict between the convergence speed and the steady-state accuracy. To solve this problem, this paper proposes an improved variable step size adaptive harmonic detection method. The algorithm uses the sliding integrator to find the feedback that can truly reflect the tracking situation, and substitutes it into the step size iteration formula based on L2 norm to achieve the step size adjustment. In order to further verify the superiority and practicability of the improved algorithm, the algorithm is applied to the photovoltaic-active power filter (PV-APF) system, and simulation verification is performed in MATLAB. The simulation results show that under the same simulation conditions, the convergence speed of the improved algorithm is half a cycle faster than that of the traditional method, and the steady-state error is smaller. In the application of PV-APF system, under normal light, the dual functions of grid-connected photovoltaic system and harmonic compensation are realized. The total harmonic distortion (THD) of the system is 2.09%, which effectively improves the power quality of photovoltaic system.

The noise cancelation effects caused by spike-frequency adaptation in single neurons

Spike-frequency adaptation, which reduces the firing rate of a neuron during a constant stimulus, is a prominent property observed in many neurons. In this work, we studied the effects of the $$I_{\mathrm{AHP}}$$ spiking adaptation on the information transmission and efficiency of the Morris–Lecar (ML) neuron model in the spike-timing coding scheme. We showed that this kind of adaptation caused non-trivial spiking dynamics when the input rate was high. Under the stimulation of high-rate inputs, although $$I_{\mathrm{AHP}}$$ adaptation neurons could not outperform non-adaptation neurons in terms of information rate, $$I_{\mathrm{AHP}}$$ adaptation neurons yielded higher coding efficiency than non-adaptation neurons. We also found that the noise could enlarge the range of input rates that the adaptation takes effect to enhance coding efficiency. Increasing the calcium-activated $$\hbox {K}^{+}$$ current could also extend the range of input rates in which adaptation takes effect. Therefore, we argue that the $$I_{\mathrm{AHP}}$$ adaptation mechanism may play a role of adaptive noise cancelation mechanism in neuronal information processing, i.e., it maintains information transmission when neurons receive low-rate inputs but substantially enhancing coding efficiency for high-rate inputs and highly noise environments by suppressing the spike train variability caused by the noise.

Design of an adaptive noise canceller for improving performance of an autonomous underwater vehicle-towed linear array

Noise radiated from an autonomous underwater vehicle (AUV) is one of the major sources of interference that impact the acoustic performance of its towed array sensor, especially during passive detection under low ambient noise conditions. In this paper, an adaptive noise canceller (ANC) based on the partitioned fast block least-mean-square adaptive algorithm is designed and applied to mitigate the AUV-radiated noise to improve the detection performance of a linear array towed by an AUV platform. The gain, defined as the ratio of the output signal-to-noise ratio (SNR) to the input SNR, is taken as the metric for our ANC. Results obtained from processing the experimental data indicate that the gain of the ANC is up to 20 dB.

Experimental study on enhancement of defect detection and defect size estimation in deep groove ball bearing

Rolling elements bearings are widely used in rotating equipment and machines to support load and to reduce friction. The presence of micron sized defects on the mating surfaces of the bearing components can lead to failure through passage of time. The large size defects on bearing elements can be detected/identified by time domain and frequency domain analysis of its vibration signals. However, it becomes difficult to detect local bearing defects at their initial stage either due to their smaller size or presence of noise. In the present experimental study, detection of local defects like crack and pits on bearing races have been carried out. Vibrations generated by healthy bearing and bearing having circular or rectangular defect on either race of bearing have been analyzed using MATLAB software. Signal to noise ratio of vibration signal has been enhanced through self- adaptive noise cancellation. Moreover, width of rectangular defects and diameter of circular defects have been estimated through ‘db5’ wavelet. The estimated defect sizes have been compared and validated through measured actual crack width or pit diameter. Accuracy of results proves that wavelet analysis of time domain signal can be used with confidence to estimate the width of fatigue crack and pit of the bearing races.

A Pulse Wave Monitor with Adaptive Filtering of Motion Artifacts

The structural design of a wearable device for everyday continuous pulse wave monitoring is considered. A technique for digital processing of pulse wave signals has been developed based on bandpass filtering and adaptive noise cancellation. A block diagram of the measuring channel of the pulse wave monitor is suggested.

Improvement of LMS adaptive noise canceller using uniform Poly-phase digital filter bank

<span>This paper presents the Least Mean Square (LMS) noise canceller using uniform poly-phase digital filter bank to improve the noise can-cellation process. Analysis filter bank is used to decompose the full-band distorted input signal into sub-band signals. Decomposition the full-band input distorted signal into sub-band signals based on the fact that the signal to noise ratio (S/N) is inversely proportional to the signal bandwidth. Each sub-band signal is fed to individual LMS algorithm to produce the optimal sub-band output. Synthesis filter bank is used to compose the optimal sub-band outputs to produce the final optimal full-band output. In this paper, m-band uniform Discrete Fourier Transform (DFT) digital filter bank has been used because its computational complexity is much smaller than the direct implementation of digital filter bank. The simulation results show that the proposed method provides the efficient performance with less and smooth error signal as compared to conventional LMS noise canceller.</span>

Performance Analysis of LMS, NLMS Adaptive Algorithms for Speech Enhancement in Noisy Environment

The speech enhancement is an important technique to remove noise from corrupted speech signal. Here several Adaptive Algorithms were proposed to improve quality of speech signal. In this paper to estimate the speech enhancement performance with variety of noise reduction algorithms using adaptive filters like LMS, NLMS. Simulations were performed on noisy data which was prepared by adding machinegun, Factory, vehicle and Traffic noise at 0dB, 5dB and 10dB SNR levels to clean speech samples The performance comparison of adaptive noise cancellation (ANC) system using LMS and NLMS algorithms was carried by means of signal to noise ratio (SNR), mean square error (MSE) and root mean square error (RMSE). Based on performance analysis, the NLMS algorithm was found to be a better optimal adaptive noise canceller for speech signal

Fractional Fourier Transform Based Adaptive Filtering Techniques for Acoustic Emission Signal Enhancement

Acoustic emission technique (AET) is a well-known non-destructive testing method used for the detection of crack growth and monitoring structural integrity of components. The main limitation in the application of AET is the fact that acoustic emission (AE) signal is often affected by background noise. A new approach is proposed in this paper to reduce the background noise and to improve the accuracy of AE signal detection. Various noise reduction methods have been applied and studied for AE signal enhancement so far. Some of the studies proved that self-adaptive noise cancellation (SANC) and adaptive noise cancellation (ANC) are better techniques for denoising the AE signal. This paper proposes the use of ANC and SANC schemes based on Fractional Fourier Transform (FrFT) for AE signal enhancement. FrFT is the generalization of the classical Fourier Transform (FT). The use of FrFT gives an advantage of an additional degree of freedom (angle of rotation in time–frequency plane) over the traditional Fourier transform and could provide improved performance. In this method, the noisy signal is rotated in time- frequency plane to extract the signal in Fractional Fourier domain (FrFD). Two adaptive filters viz. least mean squares and normalized least mean squares are studied for FrFD based ANC approach. The performance of the proposed method is validated using real AE signals acquired from three different experiments: pencil lead break test, composite drilling test and concrete compression test. Results are compared with the time and frequency domain adaptations. Moreover, the results have also been compared with the frequency domain adaptation techniques. Results have demonstrated that FrFD based ANC approach outperforms the conventional time domain and frequency domain adaptive filtering with an improved signal to noise ratio and significantly reduces mean square error.

Fiber optic gyro signal denoising based on variational mode decomposition and adaptive noise cancellation

The output signal of the fiber optic gyro (FOG) often contains a large amount of noise, which makes it difficult to model and calibrate. Therefore, it must be denoised. In this paper, an adaptive noise cancellation method based on variational mode decomposition (VMD) is proposed. The FOG signal was decomposed into a series of mode components with different center frequencies, and the correlation coefficient method was used to determine the number of decomposition layer. The low-frequency signal and the high-frequency noise components were distinguished by the frequency distribution of each component. They were respectively accumulated to use as the ready denoising signal and the reference noise, then obtaining the denoised signal by adaptive noise cancellation. Using FOG measured data, the proposed method was taken into experiment compared with traditional methods. Allan variance analysis verified that the proposed method effectively suppresses the noise in the FOG signal.

Control and Operation of Dynamic Voltage Restorer With Online Regulated DC-Link Capacitor in Microgrid System

This article presents a dynamic voltage restorer (DVR) topology based on the adaptive noise canceling (ANC) technique, which can be used for both voltage compensation and harmonic mitigation. Furthermore, this article aims to investigate the DVR performance when installed in a microgrid (MG) during both normal operation of the utility and during utility disturbances. One of the main objectives of this article is to reduce the cost of inverter-based DVR by reducing both the size of the dc-link capacitor and rating of switching elements. The voltage of the dc-link capacitor is regulated to low voltage level using a transformer and a pulsewidth modulation (PWM) rectifier to achieve both effective voltage regulation and drawing a sinusoidal line current from the grid and thus not to contribute to the increase of the THD of the utility current. Furthermore, the voltage across the switches can be limited to low value by an adequate design of dc-link capacitor size, capacitor voltage, and sag level to be compensated. Finally, the effectiveness and fast response of the proposed DVR for the compensation of voltage disturbances and current harmonics is confirmed by simulation using MATLAB/Simulink during the steady-state and transient operations to analyze the performance of the scheme under different operating conditions.

Automatic and Adaptive Signal- and Background-ROIs With Analytic-Representation-Based Processing for Robust Webcam-Based Heart-Rate Estimation

Determining a suitable, adaptive region of interest (ROI) automatically for extracting information related to cardiac activity (signal-ROI/S-ROI), and another containing information on ambient light-fluctuation (background-ROI/B-ROI, as close as possible to the signal-ROI ), and robust signal processing are important in webcam based heart-rate (HR) estimation – in real life situations. We describe a novel method of automatically determining both the ROIs. The forehead is the candidate for the S-ROI, due to its uniformity and minimum vulnerability for deformation. We first identify the skin-pixels within the face-region detected by the Viola-Jones (VJ) algorithm. The forehead-region, and a uniform sub-rectangle within it not containing hair – determined by using variance as a measure – yields the S-ROI. The B-ROI – consisting of 3 rectangles – each of the same size as that of S-ROI, at the two sides and the top of the VJ-rectangle – is used to generate a reference signal for an adaptive noise-cancellation scheme. The situation arising from (possibly simultaneous) facial expressions deforming the S-ROI , is addressed – by extracting the phase sequence associated with the analytic representation of the signal. Experiments conducted with 21 healthy subjects, using this novel array of techniques, have produced good correspondence with the ground truth obtained from a standard finger-pulse transducer – as reflected by the Bland-Altman plot.

A novel approach adaptive filtering method for electromyogram signal using Gray Wolf optimization algorithm

The proposed paper, presents the construction of adaptive noise cancellation filter based on gray wolf optimization (GWO) optimization technique.The relative investigation of different strategies uncovers that the presentation of GWO calculation is better in boisterous condition. The objective of proposed paper is structure ANC channel utilizing GWO method that improves association involving output with pure EMG signal.The results of proposed strategy are contrasted through gray wolf optimizer (GWO) and other evolutionary algorithms.The presentation of these calculations is assessed regarding signal-to-noise ratio (SSNR), mean square error (SMSE), maximum error (SME) mean, convergence rate (CR) plus correlation feature (Sr). The noise attenuation capability is tested on EMG signal contaminated with power line and ECG noise at different SNR levels. The ANC filter based on GWO technique provides 28 dB improvement in output SNR, 81% reduction in MSE, and 84% lower ME as compared to reported ANC filter based on RLS algorithm. Further, ANC filter based on GWO technique provides 7 dB improvement in output SNR, 59.5% reduction in MSE, and 69.2% lower ME as compared to recently reported ANC filter based on ABC-MR algorithm.

Combination of LMS Algorithm in Adaptive Noise Cancellation System

In the adaptive noise cancellation system, the traditional fixed step size LMS algorithm is used to filter the noise signal, but the noise signal cannot be completely cancelled out. In order to realize the two-stage filtering, this paper proposes to combine two fixed step size LMS algorithms and to combine a fixed step size LMS algorithm with a variable step size LMS algorithm based on Bessel function. The combinations of different LMS algorithms are applied to the adaptive noise cancellation system, and the MATLAB simulation experiment is performed on the noisy signal. Theoretical analysis and simulation results show that the two-stage filtering realized by combination of different LMS algorithms is obviously better than the one-stage filtering realized by a single fixed step LMS algorithm, and the performance in terms of fast convergence speed, tracking speed and steady state error is improved.

Adaptive Noise Cancellation from Speech Signals using Variablestep Sizealgorithm

Noise cancellation from the speech signal is the most importanttask in applications like communications, hearing aids, speech therapy and many others. This allows providing good resolution speech signal to the user. The speech signals are mostlycontaminated due to the several natural as well as manmade noises. As the characteristics of these noises random in its nature filtering techniques with fixed coefficients are not suitable for noise cancellation task. Hence, in this work an adaptive noise canceller algorithmhas driven for enhancement of speech signal applications which has the capability to update its weight coefficients based on the statistical nature of the undesired component in the actual speech signal. In our experiments in order to achieve better convergence rate as well as filtering capability we propose Step Variable Least Mean Square (SVLMS) algorithm instead of constant step parameter. The computational complexity of the speech enhancement process is also a key aspect due to the excessive length of the speech signals in realistic scenario. Hence, to reduce the computational complexity of the proposed mechanism we used Sign Regressor SVLSM (SRSVLMS), which is a hybrid realization of familiar sign regressor algorithm and the proposed SVLMS. Using these two techniques noise cancellation models are developed and tested on real speech signals with unwanted noise contaminations. The experimental outputsconfirm that the SRSVLMS based speech signal enhancement unit performs better than its counterpart with respect to convergence rate, computational complexity and signal to noise ratio increment.

Artifact Elimination from EEG Signals using Error Normalized algorithms for Brain Computer Interface Systems

In this paper some efficient and low computation complex signal conditioning algorithms are proposed in distant health tracking applications, for improvement of the electroencephalogram (EEG) signal. Few artifacts are contaminated also mask small characteristics underlying EEG signal activity in medical environments during extraction of EEG signal. Low computational difficulty filters are appealing especially within distant healthcare surveillance. Therefore, we provided several effective and less computing adaptive noise cancellers (ANCs) in this work to improve EEG signal. Most of these techniques use easy addition as well as shift calculations also attain significant convergence performance compared to other standard techniques. Real EEG signals collected using emotional EEG systems are verified for proposed implementations. Using several performances measures our studies demonstrate that techniques suggested provides best performance than prevailing methods. This methodology is suitable in the analysis of brain computer interface (BCI) applications.

All-digital background calibration of gain and timing mismatches in time-interleaved ADCs using adaptive noise canceller

This paper proposes a novel all-digital background calibration technique for gain and timing mismatches in Time-Interleaved Analog-to-Digital Converters (TIADCs) using adaptive noise canceller (ANC). The error signals due to the gain and timing mismatches are expressed in linear regression terms, producing the estimation problem of ANC. The gain and clock skew coefficients are estimated by maximizing the output signal-to-noise ratio in ANC system. The correction is simple by subtracting the re-constructed errors from the TIADC output. The proposed calibration technique eliminates the input spectrum constants as well as removes high-pass filters, which are required in the conventional free-band based calibration technique. In order to validate the proposed approach, simulations are carried out for an 11-bit, 2.7GS/s four-channel TIADC for various input signals. Results show that the proposed calibration produces excellent performance in terms of mismatch distortion suppression. It achieves the SNDR and SFDR improvement of 19dB and 49dB, respectively. Moreover, the synthesized design with hardware co-simulation on Xilinx Kintex-7 field-programmable gate array (FPGA) platform consumes only 7.36% of the hardware resources of the FPGA chip and reduces the mismatch tone level up to -87dB.

A Hearing Device With an Adaptive Noise Canceller for Noise-Robust Voice Input

This paper presents a hearing device with an adaptive noise canceller for noise-robust voice input. The hearing device has an inner-ear and an outer-ear microphone to collect ear-canal speech signal and the outer-ear environmental signal, respectively. The environmental signal drives an adaptive filter to generate a noise replica which is subtracted from the inner-ear microphone signal to cancel the noise. Coefficients of the adaptive filter are updated by an NLMS (normalized least mean square) algorithm with an SNR-controlled stepsize for small speech distortion. The adaptive noise canceller is realized on a digital signal processor chip with 39 McPS (million cycles per second) for 64 taps. Evaluation results in speech recognition show that a word recognition rate of 89% is achieved when the noise level is 80 dBA with 38% improvement over the inner-ear signal without adaptive noise cancellation. DSP (digital signal processor) implementation details and two prototype devices demonstrate high feasibility.