Noise Cancellation Research Articles

Correction of Dual-PRF Velocity for Operational S-Band Doppler Weather Radar

The Weather Radar Center (WRC) of the Korea Meteorological Administration (KMA) has been providing three-dimensional radar wind fields based on the “WInd Synthesis System using Doppler Measurements (WISSDOM)” in real time since February 2019. Its accuracy is significantly affected by the quality of the Doppler velocity, such as velocity aliasing. For the de-aliasing of Doppler velocity, the dual-pulse repetition frequency (dual-PRF) technique is commonly utilized for commercial Doppler weather radar. The Doppler weather radars of the KMA have extended their Nyquist velocity up to 132 m s−1 using a dual PRF of 5:4. However, the dual-PRF technique produces significant noise and loss of radial velocity. Therefore, we developed a technique for noise cancelation and recovery of radial velocity to improve the quality of WISSDOM wind fields. The proposed approach identifies and removes speckles of abnormal radial velocity by comparing the sign of the median radial velocity with the surrounding radar bins. We then recovered the eliminated radial velocity using median interpolation. To recover the losses of radial velocity over a wide area using the dual-PRF technique, we used the Velocity Azimuth Display curve-fitting technique. These techniques are straightforward, preserve spatial gradients, and suppress local extrema. We tested this technique, verified its performance, and applied it to the operational radar quality control system of the WRC from August 2021. We concluded that the process helps improve the quality of the radial velocity and the accuracy of the WISSDOM wind fields.

Single line noise cancellation using derivative of normalized least mean square algorithm

Suppression of noise in noisy speech signal is required in many speech enhancement applications like signal recording and transmission from one place to other. In this paper a novel single line noise cancellation system is proposed using derivative of normalized least mean spare algorithm. The proposed system has two phases. The first phase is generation of secondary reference signal from incoming primary signal itself at initial silence period and pause between two words, which is essential while adaptive filter using as noise canceller. Second phase is noise cancellation using proposed modified error data normalized step size algorithm. The performance of the proposed algorithm is compared with normalized least mean square (NLMS) algorithm and original error data normalized step size (EDNSS) algorithm using standard IEEE sentence (SP23) of Noizeus data base with different types of real world noise at different level of signal to noise ratio (SNR). The output of proposed, NLMS and EDNSS algorithm are measured with output SNR, excessive mean square error (EMSE) and misadjustment (M). The results clearly illustrates that the proposed algorithm gives improved result over conventional NLMS and EDNSS algorithm. The speed of convergence is also maintained as same conventional NLMS algorithm.

A novel adaptive noise canceling system for urea NQR spectrum analysis

Due to its practical ease of use and sufficiently high reliability, Nuclear Quadrupole Resonance (NQR) has largely been used in remote sensing-based detection of quadrupole nuclei, especially Nitrogen-containing materials. Since the received signal of free induction decay (FID) is inherently weak in this technique, the noise and the interference of the environment would dramatically impair the performance of the detecting systems. To handle such challenges, an adaptive noise canceling method was employed to improve the signal to noise and interference ratio (SNIR) in FID signals. FID signals, noise, and interference were received by the main antenna, while similar noise and interference would be received by the auxiliary antenna. The proposed system significantly reduced noise and interference through the digital noise and interference cancellation algorithm, pulse integration, averaging, and singular spectrum analysis (SSA). Urea powder was empirically analyzed outdoors, suggesting that the proposed adaptive noise and interference cancellation (ANIC) system, effectively suppressed noise and interference by 41.13 dB and amplified the FID signal by 53.36 dB. The strongest signal was detected at 2870.4 kHz.

Unsourced Random Access Over Frequency-Selective Channels

We propose and study unsourced random access over frequency-selective channels via orthogonal frequency division multiplexing, employed to mitigate the channel effects. The decoder utilizes a compressed sensing-based joint activity detection and channel estimation algorithm coupled with an algorithm treating interference as noise and successive interference cancellation. Our results demonstrate that the proposed method offers a competitive performance with grant-based frequency division multiple access; and that, the performance loss with estimated channel state information is less than 2 dB when the number of active users is not very large.

Common Mode Conductive Noise Cancellation for Multiphase Converter Using Auxiliary Winding

A multiphase DC-DC converter consisting of a coupled inductor and fast switching devices enables power electronics equipment to achieve a high power density. However, with such devices, the fast voltage transition generates substantial common mode (CM) noise. However, there are very few studies on CM noise reduction for multiphase converters using a coupled inductor. Therefore, in this paper, a CM noise reduction method is proposed for a multiphase DC-DC converter that hardly lowers the power density.

A Background Test and Calibration Technique of Nonlinear Mismatches in MEMS Oscillating Accelerometers

MEMS accelerometers have benefits of low cost and small volume, however their long-term stability is limited by the fabrication mismatches. This paper proposes a self-test and calibration technique for differential MEMS accelerometers, to reduce the effect of flicker noise in reference voltages due to nonlinear stiffness mismatch in two channels. Through detecting the frequency response induced by the sinusoidal test signal, the oscillating amplitude of the two channels is properly adjusted to meet a theoretically derived noise cancellation condition. A numerical model is established based on practical mechanical parameters of the MEMS structures to simulate the proposed system, and optimize the key design parameters. Finally, a prototype including a MEMS accelerometer chip and an FPGA-based control circuit is implemented to verify the suggested approaches. Compared to that without the proposed technique, the 1-h bias-instability of the SOA is reduced from 6.31 μg to 3.44 μg. At different reference voltages from 1.09 V to 1.33 V, the decrease in instability is between 21.7 % and 45.5 %.

Enhanced Multiple Speakers’ Separation and Identification for VOIP Applications Using Deep Learning

Institutions have been adopting work/study-from-home programs since the pandemic began. They primarily utilise Voice over Internet Protocol (VoIP) software to perform online meetings. This research introduces a new method to enhance VoIP calls experience using deep learning. In this paper, integration between two existing techniques, Speaker Separation and Speaker Identification (SSI), is performed using deep learning methods with effective results as introduced by state-of-the-art research. This integration is applied to VoIP system application. The voice signal is introduced to the speaker separation and identification system to be separated; then, the “main speaker voice” is identified and verified rather than any other human or non-human voices around the main speaker. Then, only this main speaker voice is sent over IP to continue the call process. Currently, the online call system depends on noise cancellation and call quality enhancement. However, this does not address multiple human voices over the call. Filters used in the call process only remove the noise and the interference (de-noising speech) from the speech signal. The presented system is tested with up to four mixed human voices. This system separates only the main speaker voice and processes it prior to the transmission over VoIP call. This paper illustrates the algorithm technologies integration using DNN, and voice signal processing advantages and challenges, in addition to the importance of computing power for real-time applications.

Active Noise Cancellation in Microsoft Teams Using AI & NLP Powered Algorithms

Active Noise Cancellation in Microsoft Teams Using AI & NLP Powered Algorithms

Prescribed-time distributed observer based practical predefined-time leader-follower output consensus of second-order multiagent system with communication noises

This paper studies predefined-time leader-follower consensus issue for second-order multi-agent system (MAS) with communication noises over digraph. Crosstalk resistant adaptive noise canceller is utilized to filter out communication noise. Next, with the filtered states received from its neighbors, a prescribed-time distributed observer is first proposed, which requires no global information and provides prescribed-time leader's states estimation under noisy communication environment and directed communication graph. Then, an adaptive predefined-time control strategy is presented to counteract the mismatched disturbance caused by estimation error and achieve practical predefined-time leader-follower output consensus of the studied MAS. Lyapunov stability analysis proves that the developed distributed observer achieves prescribed-time convergence and the developed tracking controller achieves predefined-time convergence. Finally, the developed control strategy is applied to consensus tracking of networked single-link robotic manipulators and simulation results show its effectiveness.

A computationally efficient feedforward time–frequency-domain hybrid active sound profiling algorithm for vehicle interior noise

Active sound profiling (ASP) is gaining ever-increasing attention owing to its powerful ability to balance the sound pressure levels of certain frequency points or frequency bands of target noise for sound quality enhancement. Most of the current ASP algorithms, however, only focus on narrowband or broadband noise alone and the computational efficiency is very low. To alleviate this problem, a feedforward time–frequency-domain hybrid active sound profiling (TFD-HASP) algorithm is proposed in this paper. The algorithm consists of three subsystems, in which the narrowband ASP (NASP) subsystem utilizes the time-domain internal model FXLMS algorithm along with the local secondary path estimation approach, the broadband ASP (BASP) subsystem is based on the delayless frequency-domain internal model FXLMS algorithm, and the sinusoidal noise canceller (SNC) subsystem is designed to eliminate narrowband interference to the BASP subsystem. In this way, the presented TFD-HASP algorithm not only has good spectral reshaping capability for both narrowband and broadband noise components, but also requires notably low computational effort. A series of simulations are performed to evaluate the performance of the TFD-HASP algorithm. In addition, the comfortable sound quality of a real vehicle interior noise at high speed is designed and realized. Experimental results demonstrate that the proposed algorithm achieves satisfactory active sound profiling for noise with both narrowband and broadband characteristics.

Adaptive parallel filter method for active cancellation of road noise inside vehicles

Active noise cancellation (ANC) is an efficient and effective method for suppressing low frequency disturbances generated by tyre–road interaction in a vehicle. There are significant differences in road noise inside the vehicle under different driving conditions. However, fixed-filter ANC strategies are unable to achieve superior noise attenuation for different types of noise, and conventional adaptive algorithms require prolonged adaptation which is detrimental to dynamic noise reduction. Thus, this study proposes an adaptive parallel filter method (APFM) that quickly responds to and suppresses varying vehicle interior road noises. The APFM is divided into two phases: the tuning phase and control phase. In the tuning phase, a set of potential noises is adopted to train the corresponding optimal fixed filters. During the control phase, the most appropriate parallel control filter is first selected for each frame based on a selection mechanism to minimise the energy of the estimated error signal. Then, a delayless variable step size normalised frequency-domain block adaptive filter algorithm is adopted to update the selected control filter. In addition, the computational complexity of the proposed APFM is analysed. Numerous simulations and real time in-vehicle experiments conducted using multi-channel ANC headrests have confirmed the feasibility and effectiveness of the proposed algorithm. It is evident that the APFM achieves fast noise reduction response and low steady-state error in attenuating varying in-vehicle road noise, and retains superior performance in the presence of non-pre-trained disturbances.

Underwater Active Noise Cancellation Combining Kalman Filter with FxLMS

Underwater Active Noise Cancellation Combining Kalman Filter with FxLMS

Implementation of active cancellation of noise entering a window

Environmental noise propagating through an open window may be cancelled by a sparse array of transducers placed in the window frame. This approach towards global noise cancellation mitigates noise pollution at its source while retaining the light and ventilation received from an open window. While theoretical work has been established for this process, all calculations have relied on far field equations leaving the near field not well understood. This was resolved by modeling the system in COMSOL Multiphysics with increasing complexity. Experimental implementation revealed additional flaws in the design including undesired vibrations and insufficient power from the key transducers. Vibrations in the window’s crossbar were identified via accelerometers and suppressed by the addition of an aluminum brace. Experimental results will be presented.

On optimum speaker placement with a model-based approach for indoor local active noise control applications

In this study, a control system actively canceling the noise will be developed through a model-based algorithm to reduce the noise generated in the room on individuals in an environment as much as possible. The model-based design approach is preferred because it is convenient for optimization and can be applied much more effortlessly than real experiments; it is applied using the finite difference method. The model developed within this scope has been validated by comparing it with the acoustic test data performed in an empty room. The positioning of loudspeakers was accomplished through a heuristic search algorithm to obtain the highest local cancellation of noise originating in the acoustic cavity from any position(s) in the targeted area. The feedforward adaptive control system chosen to drive the control loudspeakers is implemented using the least mean squares algorithm. The efficiency of the designed system under different noise conditions was examined, and two of them were included in the study.

The Study on Air Force Pilot’s Recognition about Cockpit Noise to Foster Aviation Safety by the Use of Active Noise Cancellation (ANC)

공군 조종사들이 비행 중에 겪는 지속적이고 강도 높은 소음은 조종사의 생리적(physiological) 및 심리적(psychological) 상태에 부정적인 영향을 미칠 수 있다. 이는 조종사의 비행 능력(performance)에 부정적인 영향을 주게 되며 임무 완수 및 비행 안전을 저해시키는 치명적인 결과로 이어질 수 있다. 대한민국 공군은 조종사들의 청력 보호를 위해 수동 소음 감쇠(Passive Noise Cancellation, PNC) 및 능동 소음 감쇠(Active Noise Cancellation, ANC) 기술이 적용된 헤드셋 및 헬멧을 사용 중이다. 그러나, 소음 저감 기술이 조종사의 청력 보호, 비행 능력, 및 비행 안전에 미치는 효용성에 대한 공군 조종사의 인식은 아직 연구된 바가 없다. 따라서 본 연구는 소음과 관련된 이론적 배경을 고찰하였고, 이후 설문조사를 통해 공군 조종사들(n=154)의 조종석 내 소음 및 소음 감쇠 기술에 대한 인식을 분석하였다. 분석 결과, 능동 소음 감쇠(ANC) 기술이 적용된 헤드셋 및 헬멧의 사용은 소음이 조종사의 생리적 상태에 미치는 영향에는 유의미한 효과가 없지만(<italic>p</italic>=0.402), 심리적 상태에 미치는 영향은 유의미하게 감소시키는 것으로 나타났다(<italic>p</italic>&lt;0.001). 따라서, 능동 소음 감쇠(ANC) 기술이 적용된 비행 헤드셋 및 헬멧 사용의 필요성을 강조하였고, 이를 통해 조종사의 비행 능력(performance) 저하 방지 및 비행 안전 증진에 기여하고자 한다.

Linear time-invariant (LTI) modeling for aerial and underwater acoustics

Most newcomers to acoustic signal processing understand that linear time-invariant (LTI) filters can remove out-of-band noise from time series signals. What many acoustics researchers may not realize is that LTI models can be applied much more broadly, including to non-linear and time-variant systems. This presentation covers an overview of the autoregressive (AR), moving-average (MA), and autoregressive moving-average (ARMA) family of LTI models and their many useful applications in acoustics. Examples include analytic time-frequency processing of multi-component echolocation signals, fractional-delay filtering for acoustic time series simulations, broadband acoustic array beamforming, adaptive filtering for noise cancelation, and system identification for acoustic equalizers (i.e., flattening the frequency response of a source-receiver pair). This talk serves as a brief tutorial and inspiration for researchers who want to expand their use of signal processing, especially those in the fields of animal bioacoustics, aerial acoustics, and underwater acoustics.

Noise Cancellation of a Train Electric Traction System Fan Based on a Fractional-Order Variable-Step-Size Active Noise Control Algorithm

In this study, an active noise control (ANC) algorithm based on fractional order with a variable step size is proposed to improve the convergence performance of an integer-order ANC algorithm and achieve noise cancellation of a train electric traction system fan. The algorithm optimized the convergence parameters by constructing a time-varying function related to the variation of an error signal to improve the convergence speed of the algorithm, and the introduction of fractional calculus in the algorithm to control the update iteration of the filter weight coefficients, which could improve the control accuracy of the algorithm and reduce the steady-state error. And we give proof of the convergence of the algorithm and compared the noise reduction effect of the system under different orders to obtain the optimal order. The algorithm is compared with other traditional algorithms, and it is proved that it is superior to traditional algorithms in terms of convergence speed, steady-state error, and error attenuation. The experimental results demonstrate that the algorithm exhibited a good noise reduction effect on the fan noise of the train electric traction system of 0–500 Hz and the average noise reduction was about 1.9–4.1 dB.

Dental drill noise reduction using a commercially-available earplug device.

The dental handpiece noise in a dental surgery is concerning to both patients and staff as a major cause of dental phobia in patients and potential hearing loss in clinical staff. High-frequency noise generated by dental handpieces is considered to be the worst of the many noises in a dental clinic. Methods to reduce this noise have been proposed and either passive or active noise reduction headphones are often suggested. However, in a dental surgery environment, the need for good verbal communication with the patient needs to be maintained. As a result, this paper aims to evaluate one proprietary anti-noise device considered suitable for this specific purpose. Lab-based experiments were set up and carried out to evaluate QuietOn, using GRAS 43AG-1 Ear and Cheek Simulator to mimic a section of the human head and ear to represent the acoustic characteristics of an actual ear. Two types of dental drill noise recordings, one for electric motor-driven and another one for air turbine-driven, were played back through high-definition speakers. Sound data captured by the simulator are then visualised and plotted using MATLAB for analysis. QuietOn is effective at low frequencies (< 1kHz). However, when dealing with high-frequency noise such as dental handpieces it is ineffective yet subdues verbal communication. Further development of passive or active noise cancellation earplugs is still needed to target dental handpiece noise while maintaining verbal communication.

Development of Adaptive Spectrum Noise Cancellation Technique for Enhancing Heartbeat Rate Monitoring

Most of the Heart Rate Monitor (HRM) seems to be a portable monitoring device that can measure and displays real-time heartbeat rate in addition to storing heartbeat rate data for future studies. It is generally used to capture heart rate data during various types of physical exercise. Heart rhythm problems arise whenever the electrical impulses that coordinate the heartbeats aren't working properly. The ineffective signaling causes the heart to beat too fast (tachycardia), overly slowly (bradycardia), or sporadically. In present study, a focused approach on Adaptive Spectrum Noise Cancellation (ASNC) were used for enhancing heartbeat rate monitoring for humans. It finds that simply a photodetector and a source of light are applied to the surface of the skin to monitor the proportional changes in the blood flow. It is concluded that for accurate measurement of heart rate while moving or running, the proposed device ASNC utilizes the application of embedded accelerometer and gyroscope sensors to identify and eradicate the artifacts adaptively. In future, the proposed model may adopted for modification and commercialization of wearable heart-rate sensors based upon photoplethysmography (PPG), it would be better in terms of utility as well as adoptability.

Active Noise Cancellation in Microsoft Teams Using AI &amp; NLP Powered Algorithms

The normal method for analyzing technology is formulating many search queries to extract patent datasets and filter the data physically. The purpose of filtering the collected data is to remove noise to guarantee accurate information analysis. With the advancement in technology and machine learning, the work of physical analysis of the patent can be programmed so the system can remove noise depending on the results based on the previous data. Microsoft team generates a new artificial intelligence model that provides solutions on how individuals respond to speakers. Microsoft team, workplace, Facebook, and Google collected data from many active users hence developing artificial intelligence to minimize distracting background noise, barking and typing during the call.