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.

Using LMS Adaptive Filter in Direct Wave Cancellation

The way to use the least-mean-square (LMS) arithmetic to cancel the direct wave for a passive radar system is introduced. The model of the direct wave is deduced. By using the LMS adaptive FIR filter, the software solution for FM passive radar system is developed instead of the hardware consumption of the existent experiment system of passive radar. Further more some simulative results are given. The simulative results indicate that using LMS arithmetic to cancel the direct wave is effective.

ADAPTIVE HARMONIC CANCELLATION APPLIED IN ELECTRO-HYDRAULIC SERVO SYSTEM WITH ANN

The method for harmonic cancellation based on artificial neural network (ANN)is proposed. The task is accomplished by generating reference signal with frequency that should beeliminated from the output. The reference input is weighted by the ANN in such a way that it closelymatches the harmonic. The weighted reference signal is added to the fundamental signal such thatthe output harmonic is cancelled leaving the desired signal alone. The weights of ANN are adjustedby output harmonic, which is isolated by a bandpass filter. The above concept is used as a basis forthe development of adaptive harmonic cancellation (AHC) algorithm. Simulation results performedwith a hydraulic system demonstrate the efficiency and validity of the proposed AHC control scheme.

Multi-channel differencing adaptive noise cancellation with multi-kernel method

Although a various of existing techniques are able to improve the performance of detection of the weak interesting sig- nal, how to adaptively and efficiently attenuate the intricate noises especially in the case of no available reference noise signal is still the bottleneck to be overcome. According to the characteristics of sonar arrays, a multi-channel differencing method is presented to provide the prerequisite reference noise. However, the ingre- dient of obtained reference noise is too complicated to be used to effectively reduce the interference noise only using the clas- sical linear cancellation methods. Hence, a novel adaptive noise cancellation method based on the multi-kernel normalized least- mean-square algorithm consisting of weighted linear and Gaussian kernel functions is proposed, which allows to simultaneously con- sider the cancellation of linear and nonlinear components in the reference noise. The simulation results demonstrate that the out- put signal-to-noise ratio （SNR） of the novel multi-kernel adaptive filtering method outperforms the conventional linear normalized least-mean-square method and the mono-kernel normalized least- mean-square method using the realistic noise data measured in the lake experiment.

Time-shared channel identification for adaptive noise cancellation in breath sound extraction

Noise artifacts are one of the key obstacles in applying continuous monitoring and computer-assisted analysis of lung sounds. Traditional adaptive noise cancellation (ANC) methodologies work reasonably well when signal and noise are stationary and independent. Clinical lung sound auscultation encounters an acoustic environment in which breath sounds are not stationary and often correlate with noise. Consequendy, capability of ANC becomes significantly compromised. This paper introduces a new methodology for extracting authentic lung sounds from noise-corrupted measurements. Unlike traditional noise cancellation methods that rely on either frequency band separation or signal/noise independence to achieve noise reduction, this methodology combines the traditional noise canceling methods with the unique feature of time-split stages in breathing sounds. By employing a multi-sensor system, the method first employs a high-pass filter to eliminate the off-band noise, and then performs time-shared blind identification and noise cancellation with recursion from breathing cycle to cycle. Since no frequency separation or signal/noise independence is required, this method potentially has a robust and reliable capability of noise reduction, complementing the traditional methods.

Adaptive digital self-interference cancellation based on fractional order LMS in LFMCW radar

Adaptive digital self-interference cancellation(ADSIC)is a significant method to suppress self-interference and improve the performance of the linear frequency modulated continuous wave(LFMCW)radar.Due to efficient implementation structure,the conventional method based on least mean square(LMS)is widely used,but its performance is not sufficient for LFMCW radar.To achieve a better self-interference cancellation(SIC)result and more optimal radar performance,we present an ADSIC method based on fractional order LMS(FOLMS),which utilizes the multi-path cancellation structure and adaptively updates the weight coefficients of the cancellation system.First,we derive the iterative expression of the weight coefficients by using the fractional order derivative and short-term memory principle.Then,to solve the problem that it is difficult to select the parameters of the proposed method due to the non-stationary characteristics of radar transmitted signals,we construct the performance evaluation model of LFMCW radar,and analyze the relationship between the mean square deviation and the parameters of FOLMS.Finally,the theoretical analysis and simulation results show that the proposed method has a better SIC performance than the conventional methods.

Variable step-size affine projection algorithm based on global speech absence probability for adaptive feedback cancellation

A novel approach is proposed for improving adaptive feedback cancellation using a variable step-size affine projection algorithm(VSS-APA) based on global speech absence probability(GSAP).The variable step-size of the proposed VSS-APA is adjusted according to the GSAP of the current frame.The weight vector of the adaptive filter is updated by the probability of the speech absence.The performance measure of acoustic feedback cancellation is evaluated using normalized misalignment.Experimental results demonstrate that the proposed approach has better performance than the normalized least mean square(NLMS) and the constant step-size affine projection algorithms.

Adaptive Step Size Control of LMS-based Interference Cancellation for ICS Repeater in Wibro Environment

为高率数据传播的支持和房间范围的延期的 repeater 的使用为 Wibrc 系统是必要的，它基于 IEEE 8M.16e standardization.Generally ，如果在播送并且收到天线 isnot 之间的分离足够，由于反馈，从播送的原版的信号表明的 repeater 和干扰的摆动可以是 ocerur.Hence ，干扰取消系统( ICS )应该作为 repeater

Adaptive cancellation of Es layer interference using auxiliary horizontal antennas

Based on a dual-polarization high-frequency wave radar system, an adaptive system using horizontal antennas for the suppression of the Es layer interference （ELI） is deseribech The data received from the horizontal antennas were correlated with the data received from the Vertically Polarized Antennas （VPAs） to estimate and cancel the interference adaptively in the VPAs. Suppressing the interference after each coherent integration time interval, about 25 dB signal-to-interference ratio is expected with the experimentally derived data.

A new method of lung sounds filtering using modulated least mean square—Adaptive noise cancellation

Advanced processing of lung sound (LS) recording is a significant means to separate heart sounds (HS) and combined low frequency noise from instruments (NI), with saving its characteristics. This paper proposes a new method of LS filtering which separates HS and NI simultaneously. It focuses on the application of least mean squares (LMS) algorithm with adaptive noise cancelling (ANC) technique. The second step of the new method is to modulate the reference input r1(n) of LMS-ANC to acquiesce combining HS and NI signals. The obtained signal is removed from primary signal (original lung sound recording-LS). The original signal is recorded from subjects and derived HS from it and it is modified by a band pass filter. NI is simulated by generating approximately periodic white gaussian noise (WGN) signal. The LMS-ANC designed algorithm is controlled in order to determine the optimum values of the order L and the coefficient convergence μ. The output results are measured using power special density (PSD), which has shown the effectiveness of our suggested method. The result also has shown visual difference PSD (to) normal and abnormal LS recording. The results show that the method is a good technique for heart sound and noise reduction from lung sounds recordings simultaneously with saving LS characteristics.

Adaptive Noise Cancellation Algorithms Implemented onto FPGA-Based Electrical Impedance Tomography System

Electrical Impedance Tomography(EIT)as a non-invasive of electrical conductivity imaging method commonly employs the stationary-coefficient based filters(such as FFT)in order to remove the noise signal.In the practical applications,the stationary-coefficient based filters fail to remove the time-varying random noise which leads to the lack of impedance measurement sensitivity.In this paper,the implementation of adaptive noise cancellation(ANC)algorithms which are Least Mean Square(LMS)and Normalized Least Mean Square(NLMS)filters onto Field Programmable Gate Array(FPGA)-based EIT system is proposed in order to eliminate the time-varying random noise signal.The proposed method was evaluated through experimental studies with biomaterial phantom.The reconstructed EIT images with NLMS is better than the images with LMS by amplitude response AR=12.5%,position error PE=200%,resolution RES=33%,and shape deformation SD=66%.Moreover,the Analog-to-Digital Converter(ADC)performances of power spectral density(PSD)and the effective number of bit ENOB with NLMS is higher than the performances with LMS by SI=5.7%and ENOB=15.4%.The results showed that implementing ANC algorithms onto FPGA-based EIT system shows significantly more accurate image reconstruction as compared without ANC algorithms implementation.

Computer Platform Adaptive Interference Cancellation Using Higher-Order Statistics

Broadband wireless interference in a computer platform is the result of multiple dynamic electromagnetic emission sources. This interference is non-Gaussian and a receiver design based on the Gaussian assumption will yield suboptimal performance. In fact, it has a double-sided K-distribution and needs to be treated differently in the design process. When dealing with this type of interference in the presence of white Gaussian noise, traditional interference/noise cancellation schemes do not produce satisfactory results. In this paper, we present an interference mitigation method which improves BER performance. We do this by using the cross-cumulant as the criterion of goodness. Specifically, our algorithm is based on higher order statistics (HOS) and is designed to reconstruct and to cancel the interference in a recursive fashion. The algorithm is tested on both BPSK and OFDM communication environments. We compare performance in terms of BER against other cancellation methods.

Frequency-Domain Wideband Acoustic Noise Cancellation System

When building an adaptive noise cancellation system for wideband acoustic signals, one can meet some difficulties in practical implementation of such a system. The major problem is related to the necessity of using real-time signal generation and processing. In this paper the active noise control system which utilizes adaptation in frequency domain is considered. It is shown that the proposed algorithms simplify practical implementation of a noise cancellation system. The results of computer simulations and prototype experiments show the effectiveness of the proposed methods. .

Least mean square error difference minimum criterion for adaptive chaotic noise canceller

The least mean square error difference （LMS-ED） minimum criterion for an adaptive chaotic noise canceller is proposed in this paper. Different from traditional least mean square error minimum criterion in which the error is uncorrelated with the input vector, the proposed LMS-ED minimum criterion tries to minimize the correlation between the error difference and input vector difference. The novel adaptive LMS-ED algorithm is then derived to update the weights of adaptive noise canceller. A comparison between cancelling performances of adaptive least mean square （LMS）, normalized LMS （NLMS） and proposed LMS-ED algorithms is simulated by using three kinds of chaotic noises. The simulation results clearly show that the proposed algorithm outperforms the LMS and NLMS algorithms in achieving small values of steady-state excess mean square error. Moreover, the computational complexity of the proposed LMS-ED algorithm is the same as that of the standard LMS algorithms.

Noise cancellation of a multi-reference full-wave magnetic resonance sounding signal based on a modified sigmoid variable step size least mean square algorithm

Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.

An Efficient Reference Free Adaptive Learning Process for Speech Enhancement Applications

In issues like hearing impairment,speech therapy and hearing aids play a major role in reducing the impairment.Removal of noise signals from speech signals is a key task in hearing aids as well as in speech therapy.During the transmission of speech signals,several noise components contaminate the actual speech components.This paper addresses a new adaptive speech enhancement(ASE)method based on a modified version of singular spectrum analysis(MSSA).The MSSA generates a reference signal for ASE and makes the ASE is free from feeding reference component.The MSSA adopts three key steps for generating the reference from the contaminated speech only.These are decomposition,grouping and reconstruction.The generated reference is taken as a reference for variable size adaptive learning algorithms.In this work two categories of adaptive learning algorithms are used.They are step variable adaptive learning(SVAL)algorithm and time variable step size adaptive learning(TVAL).Further,sign regressor function is applied to adaptive learning algorithms to reduce the computational complexity of the proposed adaptive learning algorithms.The performance measures of the proposed schemes are calculated in terms of signal to noise ratio improvement(SNRI),excess mean square error(EMSE)and misadjustment(MSD).For cockpit noise these measures are found to be 29.2850,-27.6060 and 0.0758 dB respectively during the experiments using SVAL algorithm.By considering the reduced number of multiplications the sign regressor version of SVAL based ASE method is found to better then the counter parts.

Efficient parallel adaptive array beamforming algorithm

For a large-scale adaptive array, the heavy computational load and the high-rate data transmission are two challenges in the implementation of an adaptive digital beamforming system. An efficient parallel digital beamforming （DBF） algorithm based on the least mean square algorithm （PLMS） is proposed. An appropriate method is found to partition the least mean square （LMS） algorithm into a number of operational modules, which can be easily executed in a distributed-parallel-processing fashion. As a result, the proposed PLMS algorithm provides an effective solution that can alleviate the bottleneck of high-rate data transmission and reduce the computational cost. PLMS requires less computational load than that of the conventional parallel algorithms based on the recursive least square （RLS） algorithm, as well as it is easier to be implemented to do real time adaptive array processing. Moreover, low sidelobe of the beam pattern is obtained by constraining the static steering vector with Tschebyscheff coefficients. Finally, a scheme of the PLMS algorithm using distributed-parallel-processing system is also proposed. The simulation results demonstrate that the PLMS algorithm has the same interference cancellation performance as that of the conventional LMS algorithm. Moreover, the PLMS algorithm can obtain the same good beamforming performance, regardless how the algorithm is partitioned. It is expected that the proposed algorithm will be used in a large-scale adaptive array system to deal with real time adaptive digital beamforming processing.

TRANSFORM DOMAIN CONJUGATE GRADIENT ALGORITHM FOR ADAPTIVE FILTERING

This paper proposed a new normalized transform domain conjugate gradient algorithm (NT-CGA), which applies the data independent normalized orthogonal transform technique to approximately whiten the input signal and utilises the modified conjugate gradient method to perform sample-by-sample updating of the filter weights more efficiently. Simulation results illustrated that the proposed algorithm has the ability to provide a fast convergence speed and lower steady-error compared to that of traditional least mean square algorithm (LMSA), normalized transform domain least mean square algorithm (NT- LMSA), Quasi-Newton least mean square algorithm (Q-LMSA) and time domain conjugate gradient algorithm (TD-CGA) when the input signal is heavily coloured.

Microwave and IF Cancellation of Leakage Signal for Spread Spectrum Radar

This paper discusses the requirements for the cancellation of leakage signal, analyzes in detail the principals of cancelling leakage signal in microwave and IF band, and proposes the schemes to realize adaptive cancellation. Computer analog has shown that the proposed scheme has a very high convergence speed and can reach a high cancellation ratio.

Active Power Filter Based on Adaptive Detecting Approach of Harmonic Currents

The ip-iq detection method based on instantaneous inactive power theory has been applied widely in active power filter because of its good real-time. But it needs large computation, and three-phase currents are processed as integrity, thus calculation accuracy can't be ensured. Based on adaptive interference canceling theory, this paper presents a new ad- aptive detection method for harmonic current, it is a continuously regulated closed-loop system, and its operating characteristics are almost independent of the parameter variations of the elements, thus it performs better than that based on traditional theory. At last this paper provides the simulation of active power filter including the detecting cir-cuit which proved the design is feasible and correct.